CN102722613A - Method for optimizing electronic component parameters in antenna broadband matching network by adopting genetic-simulated annealing combination - Google Patents

Abstract

The invention discloses a method for optimizing the parameters of electronic components in an antenna broadband matching network by using a genetic-simulated annealing combination. The method is based on a genetic algorithm and performs secondary optimization through a simulated annealing algorithm, which overcomes the fine-tuning ability of the genetic algorithm At the same time, the optimal individual obtained by genetic algorithm optimization is used as the initial value of the variable to be optimized by the simulated annealing algorithm, which avoids the dependence of the simulated annealing algorithm on the initial value. In addition, for the optimization of the antenna matching network, the combination method adopts the multi-objective parallel selection method to take into account the requirements of the two important technical indicators of the antenna standing wave ratio and conversion efficiency, and introduces the adaptive adjustment of the crossover and mutation operators , which is conducive to improving the calculation speed and efficiency of the algorithm. At the same time, the optimal solution retention strategy is introduced to avoid the loss of the optimal individual.

Description

Optimization Method of Electronic Component Parameters in Broadband Matching Network of Antenna Using Genetic-Simulated Annealing Combination

technical field

The invention relates to a method for optimizing parameters of an antenna broadband matching network in the field of electromagnetics, and more particularly refers to a method for optimizing parameters of electronic components in an antenna broadband matching network by using a genetic-simulated annealing combination.

Background technique

In modern electronic equipment, due to many limiting factors such as antenna performance requirements and installation conditions, it is often difficult for a single antenna design to meet the design requirements of broadband and miniaturization at the same time in many cases. For an antenna with a fixed structure, designing a broadband matching network is an effective technical means to further improve the antenna's miniaturization and broadband performance.

At present, there are mainly three design methods of broadband matching network: analytical method, numerical method and intelligent optimization method. The analytical method is based on the theory of single-matching network design, but the analytical method has obvious deficiencies in the determination of the optimal form of the gain function, the analytical expression of the source and load, etc., and it is difficult to meet the requirements of actual engineering design. Numerical methods are represented by real frequency data method and parametric technology method. Compared with the analytical method, the numerical method has obvious advantages, but there are also some inherent disadvantages, such as difficulty in obtaining the global optimal solution, sensitivity to the selection of initial values, and poor reliability of the two optimization calculations in the real-frequency data method. With the development of global search technology, genetic algorithm (National Defense Industry Press, published in June 1999, "Principle and Application of Genetic Algorithm", authors Zhou Ming, Sun Shudong), simulated annealing algorithm (Science Press, May 2003, "Non-Numerical Parallel Algorithm-Simulated Annealing Algorithm", authors Kang Lishan, Xie Yun, You Shiyong, Luo Zuhua) and other intelligent algorithms have provided new technical means for the design of broadband matching networks. Compared with the analytical method and numerical method, the intelligent optimization method does not need to analyze the load, but seeks the optimal solution of the network element value through optimization technology based on some discrete impedance values in the load frequency band, which is more flexible in engineering applications sex and practicality. However, intelligent optimization methods are not perfect in terms of convergence speed, fine-tuning ability, computational efficiency, algorithm stability, etc. The selection and application of intelligent optimization methods require specific analysis for specific design problems.

Contents of the invention

The purpose of the present invention is to propose a method for optimizing the parameters of electronic components in the antenna broadband matching network by using the genetic-simulated annealing combination method. Parameter value, the optimal solution in the genetic algorithm is obtained by the genetic algorithm, and then the secondary optimization is carried out through the simulated annealing algorithm, which overcomes the shortcoming of the poor fine-tuning ability of the genetic algorithm, and at the same time uses the optimal solution in the genetic algorithm as a simulation The initial value of the variable to be optimized by the annealing algorithm avoids the dependence of the simulated annealing algorithm on the initial value. In addition, for the optimization problem of antenna broadband matching network, the combination method adopts the multi-objective parallel selection method, which is used to take into account the requirements of the two important technical indicators of antenna standing wave ratio and conversion efficiency, and introduces the self-adaptive Adjustment is beneficial to improve the calculation speed and efficiency of genetic algorithm. The optimal solution retention strategy is introduced to avoid the loss of the optimal individual.

A method for optimizing the parameters of the electronic components in the antenna broadband matching network using a combination of genetic-simulated annealing of the present invention comprises the following steps:

Step 1: Based on the population initialization of the genetic algorithm, obtain the variables to be optimized X={XC _a , XL _b , XR _d , XT _e };

In step 1, the capacitance, inductance and resistance in the equivalent circuit of the broadband matching network are processed by the population based on the genetic algorithm, and the variables to be optimized X={XC _a , XL _b , XR _d , XT _e } are obtained;

In the variable X to be optimized X={XC _a , XL _b , XR _d , XT _e }, XC _a represents the capacitance population, and a represents the identity of the capacitance in the equivalent circuit, such as the first capacitance population of the first capacitance C1 is denoted as XC _C1 ; in the same way, the second capacitor population is marked as XC _C2 , the third capacitor population is marked as XC _C3 , the fourth capacitor population is marked as XC _C4 , and the fifth capacitor population is marked as XC _C5 ; all capacitor populations in the equivalent circuit Expressed as XC _a ={XC _C1 , XC _C2 , XC _C3 , XC _C4 , XC _C5 } in a set form;

XL _b represents the inductance population, and b represents the identity of the inductance in the equivalent circuit. For example, the first inductance population of the first inductance L1 is denoted as XL _L1 ; similarly, the second inductance population is denoted as XL _L2 , and the third inductance population is denoted as XL L2 . is XL _L3 , the fourth inductance population is denoted as XL _L4 , the fifth inductance population is denoted as XL _L5 ; all inductance populations in the equivalent circuit are represented as XL _b = {XL _L1 , XL _L2 , XL _L3 , XL _L4 , XL _L5 };

XR _d represents the resistance population, and d represents the identification of the resistance in the equivalent circuit. For example, the first resistance population of the first resistance R1 is marked as XR _R1 ; similarly, the second resistance population is marked as XR _R2 , and the third resistance population is marked as XR R2 . is XR _R3 ; all resistance populations in the equivalent circuit are expressed as XR _d ={XR _R1 , XR _R2 , XR _R3 } in a collective form;

XT _e represents the transformer population, and e represents the input/output voltage ratio of the transformer in the equivalent circuit;

Step 2: Chromosome processing based on genetic algorithm to obtain the total population

0＜DC≤800pF；

表示标识a电容种群在第1个染色体中的变量值，

表示标识a电容种群在第2个染色体中的变量值，……，

表示标识a电容种群在第m个染色体中的变量值，也称标识a电容种群在任意一个染色体中的变量值；In step two, based on the chromosomes in the genetic algorithm, randomly generate m variable values in the variable value DC for the capacitance population XC _a

0<DC≤800pF;

Indicates the variable value that identifies the a capacitance population in the first chromosome,

Indicates the variable value that identifies the a capacitance population in the second chromosome, ...,

Indicates the variable value that identifies the a-capacitance population in the mth chromosome, also known as the variable value that identifies the a-capacitance population in any chromosome;

0＜DL≤0.1μH；

表示标识b电感种群在第1个染色体中的变量值，

表示标识b电感种群在第2个染色体中的变量值，……，

表示标识b电感种群在第w个染色体中的变量值，也称标识b电感种群在任意一个染色体中的变量值；Based on the chromosome in the genetic algorithm, randomly generate w variable values in the variable value DL for the inductance population XL _b

0<DL≤0.1μH;

Indicates the variable value identifying the b inductance population in the first chromosome,

Indicates the variable value that identifies the b inductance population in the second chromosome, ...,

Indicates the variable value that identifies the b inductance population in the wth chromosome, also known as the variable value that identifies the b inductance population in any chromosome;

0＜DR≤5kΩ；

表示标识d电阻种群在第1个染色体中的变量值，

表示标识d电阻种群在第2个染色体中的变量值，……，表示标识d电阻种群在第v个染色体中的变量值，也称标识d电阻种群在任意一个染色体中的变量值；Based on the chromosome in the genetic algorithm, randomly generate v variable values in the variable value DR for the resistance population XR _d

0<DR≤5kΩ;

Indicates the variable value identifying the d resistance population in the first chromosome,

Indicates the variable value that identifies the d resistance population in the second chromosome, ..., Indicates the variable value that identifies the d resistance population in the vth chromosome, also known as the variable value that identifies the d resistance population in any chromosome;

0.1≤DT≤10；

表示标识e变压器种群在第1个染色体中的变量值，

表示标识e变压器种群在第2个染色体中的变量值，……，

表示标识e变压器种群在第n个染色体中的变量值，也称标识e变压器种群在任意一个染色体中的变量值；Based on the chromosome in the genetic algorithm, randomly generate n variable values in the variable value DT for the transformer population XT _e

0.1≤DT≤10;

Indicates the variable value identifying the e-transformer population in the first chromosome,

Indicates the variable value identifying the e-transformer population in the second chromosome, ...,

Indicates the variable value identifying the e-transformer population in the nth chromosome, also known as the variable value identifying the e-transformer population in any chromosome;

For the variable X={XC _a , XL _b , XR _d , XT _e } to be optimized, the total population is obtained by the chromosome processing in the genetic algorithm

Step 3: use the multi-objective optimization function to allocate populations for each function in the objective function according to the parallel selection method;

中的染色体按目标函数M_目标＝{f_目标，l_目标}的个数均等地划分为第一子群体Q₁和第二子群体Q₂，对每个子群体分配目标函数M_目标＝{f_目标，l_目标}中的一个进行优化；In step three, the total population

Chromosomes in are equally divided into the first subgroup Q ₁ and the second subgroup Q ₂ according to the number of the objective function _Mobjective ={ _fobjective , _lobjective }, and each subgroup is assigned the objective function _Mobjective ={ _fobjective , one of _lobjective } to optimize;

Step 4: Obtain the optimized amount of the subpopulation by crossover mutation;

In step 4, cross-mutation is performed on the first subgroup _Q1 , and the optimized amount of each generation is retained, that is, the first optimized amount _DQ1 ; cross-mutation is performed on the second subgroup _Q2 , and the optimized amount of each generation is retained, that is, the second optimized amount The optimization quantity DQ ₂ ; the specific steps for cross-mutation to obtain the optimization quantity of each generation are:

作为当前染色体也称为当前第一染色体

Step 401: Obtain any 2 chromosomes in the first subgroup Q ₁

as the current chromosome also known as the current first chromosome

作为当前染色体

也称为当前第二染色体 Get any 2 chromosomes in the second subpopulation Q ₂

as the current chromosome

also known as the current second chromosome

表示交叉后第二个染色体；所述交叉处理依据第一适应性策略模型 $P_{c1}=\left\{\begin{array}{cc}(f_{\min }-f_{\mathrm{avg}})/(f_{\min }-f),& f\&le;f_{\mathrm{avg}}\\ 1.0,& f>f_{\mathrm{avg}}\end{array}\right.$ 进行的；P_c1表示第一子群体Q₁的交叉概率(也称为第一交叉概率)，f_min表示第一子群体Q₁中最佳个体适应度值，f表示为要交叉的两个个体中较适应的适应值，且f＝min{f₁，f₂}，f₁表示染色体

对应的驻波比优化目标f_目标的值，f₂表示染色体

对应的驻波比优化目标f_目标的值，f_avg表示第一子群体Q₁的平均适应度值；Step 402: For the current first chromosome The two individuals in are crossed over to generate a new first chromosome Indicates the first chromosome after crossover,

Indicates the second chromosome after crossover; the crossover process is based on the first adaptive strategy model $P_{c1}=\left\{\begin{array}{cc}(f_{\min }-f_{\mathrm{avg}})/(f_{\min }-f),& f\&le;f_{\mathrm{avg}}\\ 1.0,& f>f_{\mathrm{avg}}\end{array}\right.$ carried out; P _c1 represents the crossover probability of the first subgroup Q ₁ (also called the first crossover probability), f _min represents the best individual fitness value in the first subgroup _Q1 , and f represents the two The more adaptive fitness value in the individual, and f=min{f ₁ , f ₂ }, f ₁ represents the chromosome

The value of the corresponding SWR optimization target f _target , f ₂ represents the chromosome

The value of the corresponding standing wave ratio optimization target f _target , f _avg represents the average fitness value of the first subgroup _Q1 ;

中的两个个体进行交叉处理，生成新第二染色体

表示交叉后第三个染色体，

表示交叉后第四个染色体；所述交叉处理依据第二适应性策略模型 $P_{c2}=\left\{\begin{array}{cc}(l_{\max }-l)/(l_{\max }-l_{\mathrm{avg}}),& l\&GreaterEqual;l_{\mathrm{avg}}\\ 1.0,& l<l_{\mathrm{avg}}\end{array}\right.$ 进行的；P_c2表示第二子群体Q₂的交叉概率，也称为第二交叉概率，l_max表示第二子群体Q₂中最佳个体适应度值，l表示为要交叉的两个个体中较适应的适应值，且l＝max{l₁，l₂}，l₁表示染色体

对应的功率优化目标l_目标的值，l₂表示染色体

对应的功率优化目标l_目标的值，l_avg表示第二子群体Q₂的平均适应度值；to the current second chromosome

Two individuals in are crossed over to generate a new second chromosome

Indicates the third chromosome after crossover,

Indicates the fourth chromosome after crossover; the crossover process is based on the second adaptive strategy model $P_c$$2_{}=\left\{\begin{array}{cc}(l_{\max }-l)/(l_{\max }-l_{\mathrm{avg}}),& l\&Greater\; Equal;l_{\mathrm{avg}}\\ 1.0,& l<l_{\mathrm{avg}}\end{array}\right.$ carried out; P _c2 represents the crossover probability of the second subgroup _Q2 , also known as the second crossover probability, l _max represents the best individual fitness value in the second subgroup _Q2 , and l represents the two individuals to be crossover The more adaptive fitness value in , and l=max{l ₁ , l ₂ }, l ₁ represents the chromosome

The value of the corresponding power optimization target l _target , l ₂ represents the chromosome

The value of the corresponding power optimization target l _target , l _avg represents the average fitness value of the second subgroup _Q2 ;

对应的驻波比优化目标f_目标的值，f₄表示交叉后第二个染色体

对应的驻波比优化目标f_目标的值；Step 403: Compare f ₁ and f ₃ and f ₂ and f ₄ , if f ₁ ≥ f ₃ and f ₂ ≥ f ₄ , use AQ _cross instead of AQ _current ; if f ₁ < f ₃ or f ₂ < f ₄ , then AQ is _currently unchanged; f ₃ means the first chromosome after crossover

The value of the corresponding VSWR optimization target f _target , f ₄ means the second chromosome after crossover

The value of the corresponding SWR optimization target f _target ;

对应的功率优化目标l_目标的值，l₄表示交叉后第四染色体对应的功率优化目标l_目标的值；Compare l ₁ and l ₃ and l ₂ and l ₄ , if l ₁ ≤ _{l 3} and l ₂ ≤ l ₄ , use BQ _cross instead of BQ _current ; if l ₁ > l ₃ or l ₂ > l ₄ , then BQ _Currently unchanged; l ₃ means the third chromosome after crossover

The value of the corresponding power optimization target l _target , l ₄ means the fourth chromosome after crossover The value of the corresponding power optimization target l _target ;

中的两个个体分别进行变异处理，生成变异第一染色体

表示

变异后的染色体，

表示

变异后的染色体；所述的变异处理依据第三适应性策略模型 $P_{m1}=\left\{\begin{array}{cc}0.5(f_{\min }-f_{\mathrm{avg}})/(f_{\min }-f^{\&prime;}),& f^{\&prime;}\&le;f_{\mathrm{avg}}\\ 0.5,& f^{\&prime;}>f_{\mathrm{avg}}\end{array}\right.$ 进行的；P_m1表示第一子群体Q₁的变异概率(也称为第一变异概率)，f_min表示第一子群体Q₁中最佳个体适应度值，f_avg表示第一子群体Q₁的平均适应度值，f′为需要变异个体的适应度值，且

f₁表示染色体

对应的驻波比优化目标f_目标的值，f₂表示染色体

对应的驻波比优化目标f_目标的值；Step 404: For the current first chromosome

The two individuals in are mutated separately to generate the mutated first chromosome

express

mutated chromosomes,

express

Chromosomes after mutation; the mutation processing is based on the third adaptive strategy model $P_{m1}=\left\{\begin{array}{cc}0.5(f_{\min }-f_{\mathrm{avg}})/(f_{\min }-f^{\&prime;}),& f^{\&prime;}\&le;f_{\mathrm{avg}}\\ 0.5,& f^{\&prime;}>f_{\mathrm{avg}}\end{array}\right.$ carried out; P _m1 represents the mutation probability of the first subgroup Q ₁ (also known as the first mutation probability), f _min represents the best individual fitness value in the first subgroup _Q1 , and f _avg represents the first subgroup Q The average fitness value of ₁ , f' is the fitness value of the individual that needs to be mutated, and

f ₁ means chromosome

The value of the corresponding SWR optimization target f _target , f ₂ represents the chromosome

The value of the corresponding SWR optimization target f _target ;

中的两个个体分别进行变异处理，生成变异第二染色体

表示

变异后的染色体，

表示

变异后的染色体；所述变异处理依据第四适应性策略模型 $P_{m2}=\left\{\begin{array}{cc}0.5(l_{\max }-l^{\&prime;})/(l_{\max }-l_{\mathrm{avg}}),& l^{\&prime;}\&GreaterEqual;l_{\mathrm{avg}}\\ 0.5,& l^{\&prime;}<l_{\mathrm{avg}}\end{array}\right.$ 进行的；P_m2表示第二子群体Q₂的变异概率，也称为第二变异概率，l_max表示第二子群体Q₂中最佳个体适应度值，l_avg表示第二子群体Q₂的平均适应度值，l′为要变异个体的适应度值且l₁表示染色体

对应的功率优化目标l_目标的值，l₂表示染色体

对应的功率优化目标l_目标的值；to the current second chromosome

The two individuals in are mutated separately to generate the mutated second chromosome

express

mutated chromosomes,

express

Chromosomes after mutation; the mutation processing is based on the fourth adaptive strategy model $P_m$$2_{}=\left\{\begin{array}{cc}0.5(l_{\max }-l^{\&prime;})/(l_{\max }-l_{\mathrm{avg}}),& l^{\&prime;}\&Greater\; Equal;l_{\mathrm{avg}}\\ 0.5,& l^{\&prime;}<l_{\mathrm{avg}}\end{array}\right.$ carried out; P _m2 represents the mutation probability of the second subgroup _Q2 , also known as the second mutation probability, l _max represents the best individual fitness value in the second subgroup _Q2 , and l _avg represents the second subgroup _Q2 The average fitness value of , l' is the fitness value of the individual to be mutated and l ₁ means chromosome

The value of the corresponding power optimization target l _target , l ₂ represents the chromosome

The value of the corresponding power optimization target l _target ;

代替

若f₁≤f₅时，则

不变；f₅表示

变异后的染色体

对应的驻波比优化目标f_目标的值；Step 405: compare f ₁ and f ₅ , if f ₁ > f ₅ , use

replace

If f ₁ ≤ f ₅ , then

No change; f ₅ means

mutated chromosome

The value of the corresponding SWR optimization target f _target ;

代替

若f₂≤f₆时，则

不变；f₆表示

变异后的染色体

对应的驻波比优化目标f_目标的值；Compare f ₂ and f ₆ , if f ₂ > f ₆ , use

replace

If f ₂ ≤ f ₆ , then

No change; f ₆ means

mutated chromosome

The value of the corresponding SWR optimization target f _target ;

代替

若l₁≥l₅时，则

不变；l₅表示

变异后的染色体

对应的功率优化目标l_目标的值；Compare l ₁ and l ₅ , if l ₁ < l ₅ , use

replace

If l ₁ ≥ _{l 5} , then

unchanged; l ₅ means

mutated chromosome

The value of the corresponding power optimization target l _target ;

代替

若l₂≥l₆时，则

不变；l₆表示

变异后的染色体

对应的功率优化目标l_目标的值；Compare l ₂ and l ₆ , if l ₂ <l ₆ , use

replace

If l ₂ ≥ _{l 6} , then

unchanged; l ₆ means

mutated chromosome

The value of the corresponding power optimization target l _target ;

Repeat steps 401 to 405 until all the cross-mutation of chromosomes in the first subgroup _Q1 and the second subgroup _Q2 is completed, and the optimal optimization quantity of the first subgroup _Q1 of the current generation is obtained, that is, the first optimization quantity _DQ1 , the optimal optimization quantity of the second subgroup Q ₂ , that is, the second optimization quantity DQ ₂ ;

Step 5: According to the objective function M _objective = {f _objective , l _objective }, traverse all chromosomes in the optimized total population Q _total ' to obtain the current generation optimal individual in the genetic algorithm;

并把赋给I_hbest，以便下一代最优个体与当前代的最优个体进行比较，在两者中选择出较优个体，并且赋给I_hbest；In step 5, the first optimized quantity DQ ₁ and the second optimized quantity DQ ₂ are combined to form a new population, that is, the optimized total population Q _total ', and the total optimized population Q is traversed according to the objective function M _target = {f _target , l _target } All the chromosomes in _{the total} ' get the optimal individual of the current generation in the genetic algorithm

and put Assign to I _hbest so that the best individual of the next generation can be compared with the best individual of the current generation, select a better individual from the two, and assign to I _hbest ;

Judging whether the termination condition of the genetic algorithm is reached, if the termination condition of the genetic algorithm is not satisfied, then return to step 4, if the termination condition of the genetic algorithm is met, then the optimal individual I _hbest in the genetic algorithm is obtained, and retained, and then step 6 is entered; The termination condition of the genetic algorithm refers to whether the number of iteration steps K is 0. If the number of iteration steps K is not 0, return to step 3. If the number of iteration steps K is 0, the optimal individual I _hbest in the genetic algorithm is obtained. And keep it, go to step six; in order to correspond to the expression form of the variable X={XC _a , XL _b , XR _d , XT _e } to be optimized, the expression form of the optimal solution I _hbest set of electronic component parameters is $I_{\mathrm{hbest}}=\left[\begin{array}{ccccc}{C}_{C1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^2,& C_{C3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^4,& C_{C5}^{\mathrm{hbest}}\\ L_{L1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^2,& L_{L3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^4,& L_{L5}^{\mathrm{hbest}}\\ R_{R1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="R\; R"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">R</figure-callout>}}_R^2,& R_{R3}^{\mathrm{hbest}}& & \\ T_e^{\mathrm{hbest}}& & & & \end{array}\right];$

Step 6: Perform initial annealing assignment on the best individual I _hbest of the current generation to obtain the initial individual I _initial ; $I_{\mathrm{hbest}}=\left[\begin{array}{ccccc}{C}_{C1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^2,& C_{C3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^4,& C_{C5}^{\mathrm{hbest}}\\ L_{L1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^2,& L_{L3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^4,& L_{L5}^{\mathrm{hbest}}\\ R_{R1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="R\; R"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">R</figure-callout>}}_R^2,& R_{R3}^{\mathrm{hbest}}& & \\ T_e^{\mathrm{hbest}}& & & & \end{array}\right]$ Perform initial annealing assignment, then the initial individual with simulated annealing algorithm is

Step 7: Assign a trial value to the best individual I _hbest of the current generation to obtain the trial individual I _trial ;

进行试探赋值，则有新试探值个体

The initial individual for the simulated annealing algorithm

If tentative assignment is performed, there will be a new tentative value individual

Step 8: Perform simulated annealing optimization according to the objective function M _target ={f _target , l _target }, the initial individual I _initial and the trial individual I _trial to obtain the optimized solution of the electronic component parameters;

对应的目标函数M_目标＝{f_目标，l_目标}的值分别为ff₁和ll₁，ff₁表示试探个体I_试探对应的驻波比优化目标f_目标的值，ll₁表示试探个体I_试探对应的功率优化目标l_目标的值；Step 801: Calculate the trial individual

The values of the corresponding objective function M _target = {f _target , l _target } are respectively ff ₁ and ll ₁ , ff ₁ represents the value of the SWR optimization target f _target corresponding to the trial individual I _trial , and ll ₁ represents the value of the trial individual I _trial The value of the corresponding power optimization target l _target ;

Step 802: Calculate the initial individual The values of the corresponding objective function value M _target = {f _target , l _target } are ff ₂ and ll ₂ respectively, ff ₂ represents the value of _initial individual I corresponding to the SWR optimization target f _target , ll ₂ represents the initial individual I The value of the _initial corresponding power optimization target l _target ;

Step 803: Judging whether the values ff ₁ and ll ₁ of the objective function value _Mtarget ={ _ftarget , _ltarget } of the _trial individual I are better than _{the initial} corresponding objective function value _Mtarget ={ _ftarget , _ltarget } values ff ₂ and ll ₂ , if ff ₁ ≤ ff ₂ and ll ₁ ≥ ll ₂ , enter step 804, otherwise return to step 801;

Step 804: Calculate whether the trial individual I _trial satisfies the receiver function relationship $\left[\begin{array}{c}{P}_{\mathrm{VSWR}}=\exp \left(\frac{{\mathrm{ff}}_1-{\mathrm{ff}}_2}{T_{\mathrm{now}}}\right)>r\&Element;\left[\mathrm{0,1}\right]\\ P_G=\exp \left(\frac{{\mathrm{ll}}_2-{\mathrm{ll}}_1}{T_{\mathrm{now}}}\right)>r\&Element;\left[\mathrm{0,1}\right]\end{array}\right.,$ If it is satisfied, the trial individual I _will replace the _initial individual I, and enter step 805; if not, return to step 801; P _VSWR represents the receiving probability corresponding to the standing wave ratio, _PG represents the receiving probability corresponding to the conversion gain, and T _now represents The current temperature, r is a random number randomly generated with the probability of a uniform distribution function in the range [0, 1], that is, r = RAN(0, 1)

Step 805: In the annealing algorithm, the temperature is lowered at the cooling speed a, and it is judged whether the current temperature T _now is less than the cut-off temperature T _end , if T _now is greater than T _end , return to step 801; if T _now is less than or equal to T _end , then set The optimized initial individual I _{is initially used} as the electronic component parameter optimization solution I _best after genetic-simulated annealing treatment, $I_{\mathrm{hbest}}=\left[\begin{array}{ccccc}{C}_{C1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^2,& C_{C3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^4,& C_{C5}^{\mathrm{hbest}}\\ L_{L1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^2,& L_{L3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^4,& L_{L5}^{\mathrm{hbest}}\\ R_{R1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="R\; R"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">R</figure-callout>}}_R^2,& R_{R3}^{\mathrm{hbest}}& & \\ T_e^{\mathrm{hbest}}& & & & \end{array}\right].$

The advantages of the electronic component parameter optimization method in the antenna broadband matching network of the present invention are:

1. Parallel selection method

The design of antenna broadband matching network strives to maximize the conversion power gain of transmission while meeting the standing wave ratio requirements. Therefore, this is a typical bi-objective optimization problem. On the basis of the standard genetic algorithm, the multi-objective parallel selection method is introduced, which can take into account the requirements of the two performance indicators of standing wave ratio and conversion power gain. The basic idea of the parallel selection method is to first divide all individuals in the total population into some subgroups equally according to the number of objective functions, and assign a function in the objective function to each subgroup, and each function in the objective function is in the corresponding The selection operation is carried out independently in the subgroups, and some individuals with high fitness are selected to form a new subgroup, and then all these newly generated subgroups are merged into a new population, so that the "segmentation-parallelization" is continuously carried out. Select-merge operation can finally find the optimal solution of the optimization problem in the multi-objective function. The difference between this method and the linear summation of fitness functions for solving multi-objective optimization problems is that the weight of this method of mixing all individuals does not need to be selected artificially, but depends on the current generation.

2. Evolution criteria

In the calculation process of the genetic algorithm, according to the specific situation of the individual, the size of the crossover probability and the mutation probability are adaptively changed, which is beneficial to improve the calculation speed and efficiency of the algorithm. The adaptive genetic strategy can make the genetic evolution process have a better global search ability, and avoid being trapped in local extreme points with a greater probability.

3. Optimal result retention strategy

The algorithm in the present invention saves all the best results encountered in the search process, and when the algorithm process ends, compares the obtained final solution with the reserved process optimal solution, and takes the better one as the final result. In this way, the optimal solution can always be kept from being lost during the evolution process.

4. Secondary optimization of simulated annealing algorithm

After using the genetic algorithm to obtain the optimal solution in the genetic algorithm with fewer populations and fewer iterations, the optimal solution is used as the initial value of the simulated annealing algorithm for secondary optimization. This is conducive to improving the shortcomings of poor fine-tuning ability and premature phenomenon of genetic algorithm, and the optimization result of genetic algorithm is used as the initial value of simulated annealing algorithm, which also avoids the dependence of simulated annealing algorithm on the initial value. The combination of the two improves the stability of the algorithm and the dependence on the initial value as a whole.

Description of drawings

Fig. 1 is a schematic structural diagram of a traditional antenna broadband matching network.

Fig. 2 is a circuit schematic diagram of the antenna broadband matching network to be optimized.

Fig. 3 is a flowchart of the hybrid genetic-simulated annealing algorithm of the present invention.

Fig. 4 is a circuit schematic diagram of the optimized antenna broadband matching network.

Detailed ways

The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

In the present invention, the antenna broadband matching network is arranged between the antenna and the coaxial cable (see Figure 1), and the antenna broadband matching network is at least composed of inductance, capacitance, resistance and impedance converter (see Figure 2) .

In the present invention, the antenna refers to a small-sized antenna that can serve the VHF/UHF frequency band and is conformal to the airborne. The VHF, Very high frequency. The translation is: very high frequency. The VHF refers to radio waves with a frequency of 30-3000 MHz. The UHF, Ultra High Frequency. The translation is: UHF. The UHF refers to ultra-high frequency radio waves with a frequency of 300-3000 MHz.

In the present invention, the antenna broadband matching network is composed of a 2-order T-shaped L-C network. In addition, additional networks consisting of impedance transformers and resistors are introduced at the front end and back end of the antenna broadband matching network, respectively. The T-shaped L-C network has the function of filtering and matching, and the additional network is designed to modify the impedance of the antenna to make it easy to match.

Referring to shown in Fig. 2, the present invention is a kind of airborne small-sized antenna broadband matching network that realizes impedance transformation characteristic, includes the additional network that two T-shaped L-C networks, resistance and transformer T0 are formed in this broadband matching network equivalent circuit ; The T-shaped network is composed of a series L-C network and a parallel L-C network;

The first T-shaped network is composed of the first capacitor C1 connected in series with the first inductor L1, the second capacitor C2 connected in parallel with the second inductor L2, and the third capacitor C3 connected in series with the third inductor L3;

The second T-shaped network is composed of the third capacitor C3 connected in series with the third inductor L3, the fourth capacitor C4 connected in parallel with the fourth inductor L4, and the fifth capacitor C5 connected in series with the fifth inductor L5; two T-shaped networks form multiple filtering ; The resistor network in the additional network is composed of the first resistor R1, the second resistor R2 and the third resistor R3, and the additional network is to transform the impedance of the antenna;

The connection terminal of the antenna is connected to the 1 terminal of the transformer T0; the 2 terminals of the transformer T0 are grounded; the 3 terminals of the transformer T0 pass through the first capacitor C1, the first inductor L1, the third capacitor C3, the third inductor L3, and the fifth capacitor in sequence C5, the fifth inductance L5, and the first resistor R1 are connected to the 1 end of the coaxial cable (ie, the input end of the cable); the 4 ends of the transformer T0 are connected to the 2 ends of the coaxial cable (ie, the grounding end of the cable); The 2 ends of the shaft cable (that is, the grounding end of the cable) are grounded;

The 3 terminals of the transformer T0 are connected to the 4 terminals of the transformer T0 through the first capacitor C1, the first inductor L1, and the second capacitor C2 in sequence;

The 3 terminals of the transformer T0 are connected to the 4 terminals of the transformer T0 through the first capacitor C1, the first inductance L1, and the second inductance L2 in sequence;

The 3 terminals of the transformer T0 are connected to the 4 terminals of the transformer T0 through the first capacitor C1, the first inductor L1, the third capacitor C3, the third inductor L3, and the fourth capacitor C4 in sequence;

The 3 terminals of the transformer T0 are connected to the 4 terminals of the transformer T0 through the first capacitor C1, the first inductance L1, the third capacitor C3, the third inductance L3, and the fourth inductance L4 in sequence;

The 3 terminals of the transformer T0 are connected to the 4 terminals of the transformer T0 through the first capacitor C1, the first inductor L1, the third capacitor C3, the third inductor L3, the fifth capacitor C5, the fifth inductor L5, and the second resistor R2 in sequence ;

The three terminals of the transformer T0 are sequentially connected through the first capacitor C1, the first inductor L1, the third capacitor C3, the third inductor L3, the fifth capacitor C5, the fifth inductor L5, the first resistor R1, and the third resistor R3 Terminal 4 of transformer T0. In the present invention, between the secondary side of the transformer T0 and the coaxial cable access end, the antenna impedance is matched by using a multi-stage filter (consisting of two T-shaped L-C networks), so that the equivalent circuit of the broadband matching network The structure is reasonable.

The transformer and the resistance network in the airborne small antenna broadband matching network with the characteristics of impedance transformation can transform the impedance of the antenna and the load network; two T-shaped L-C networks play the role of filtering and matching, so that the impedance of the antenna After the value passes through the transformer, two T-shaped L-C networks, and the resistor network, it is close to the characteristic impedance value of the coaxial line, and finally reaches the predetermined matching.

在本技术领域，驻波比优化目标

取值越小则天线匹配网络性能越好。ω表示频率，ω_i表示天线在工作带宽内的第i个频率点，VSWR(ω_i)表示第i个频率点对应的驻波比，N表示天线在工作带宽内的频率点个数。In the present invention, the standing wave ratio of each frequency point within the bandwidth of the antenna matching network is defined from the perspective of the working bandwidth of the antenna matching network. The standing wave ratio of the first frequency point ω ₁ is denoted as VSWR(ω ₁ ), and the standing wave ratio of the second frequency point ω ₂ is denoted as VSWR(ω ₂ ). Similarly, any (i-th) frequency point ω The standing wave ratio of _i is recorded as VSWR(ω _i ), and the sum of _{the standing wave ratios of each frequency point within the bandwidth is recorded as VSWR(ω sum)=VSWR(ω 1 )} +VSWR(ω ₂ )+…+VSWR(ω _i ). In order to optimize the parameters of electronic components in the antenna matching network, the sum of the standing wave ratio of each frequency point within the bandwidth is used as the optimization target of the standing wave ratio

In this technical field, the VSWR optimization objective

The smaller the value, the better the performance of the antenna matching network. ω represents the frequency, ω _i represents the i-th frequency point of the antenna within the working bandwidth, VSWR(ω _i ) represents the standing wave ratio corresponding to the i-th frequency point, and N represents the number of frequency points of the antenna within the working bandwidth.

目

在本技术领域，功率优化目标

取值越大则天线匹配网络性能越好。ω_i表示天线在工作带宽内的第i个频率点，G(ω_i)表示第i个频率点对应的转换功率增益，P_out(ω_i)表示从匹配网络供给同轴线缆的平均功率，P_in(ω_i)表示从天线获得的最大平均功率。In the present invention, the antenna matching network bandwidth is defined from the perspective of the maximum antenna conversion efficiency, the conversion power gain of the first frequency point within the antenna matching network bandwidth is denoted as G(ω ₁ ), and the conversion power gain of the second frequency point The conversion power gain is denoted as G(ω ₂ ), similarly the conversion power gain of any (i-th) frequency point is denoted as G(ω _i ), then the sum of the conversion power gains of each frequency point within the bandwidth is denoted as G( ω _and )=G(ω ₁ )+G(ω ₂ )+...+G(ω _i ). In order to optimize the parameters of electronic components in the antenna matching network, the sum of the conversion power gains of each frequency point within the bandwidth is used as the power optimization target

head

In the art, power optimization targets

The larger the value, the better the performance of the antenna matching network. ω _i represents the i-th frequency point of the antenna within the operating bandwidth, G(ω _i ) represents the conversion power gain corresponding to the i-th frequency point, P _out (ω _i ) represents the average power supplied from the matching network to the coaxial cable , P _in (ω _i ) represents the maximum average power obtained from the antenna.

和功率优化目标

作为遗传算法与模拟退火算法相结合的目标函数，所述的目标函数采用数学集合表达形式为M_目标＝{f_目标，l_目标}。In the present invention, in order to achieve the best antenna matching network performance, the VSWR optimization target

and power optimization target

As the objective function combining the genetic algorithm and the simulated annealing algorithm, the objective function adopts a mathematical set expression as M _objective = {f _objective , l _objective }.

In order to carry out optimum value selection to the parameter values of each electronic component in the antenna broadband matching network equivalent circuit, the inventive method operates on the computer that Matlab (Matlab 2008 version or Matlab 2010 version) emulation software is installed. The computer is a modern intelligent electronic device that can automatically and high-speed perform a large number of numerical calculations and various information processing according to pre-stored programs. The minimum configuration is CPU 2GHz, memory 2GB, hard disk 180GB; operating system is windows 2000/2003/XP. The present invention adopts the optimization method combining the genetic algorithm and the simulated annealing algorithm, and the optimal matching of the parameter values of the electronic components in the specific antenna broadband matching network includes the following steps:

Step 1: Based on the population initialization of the genetic algorithm, obtain the variables to be optimized X={XC _a , XL _b , XR _d , XT _e };

In step 1, the capacitance, inductance and resistance in the equivalent circuit of the broadband matching network are processed by a genetic algorithm-based population to obtain the variable X={XC _a , XL _b , XR _d , XT _e }. In the present invention, the population processing based on the genetic algorithm is used to digitally define the electronic components in the equivalent circuit, so as to facilitate the optimal setting of each parameter in the genetic algorithm. The equivalent circuit of the broadband matching network is composed of capacitors, inductors, transformers and resistors to form an equivalent circuit structure. A better equivalent circuit structure is to meet the requirements of the airborne conformal antenna.

In the variable X to be optimized X={XC _a , XL _b , XR _d , XT _e }, XC _a represents the capacitance population, and a represents the identity of the capacitance in the equivalent circuit, such as the first capacitance population of the first capacitance C1 is denoted as XC _C1 ; in the same way, the second capacitor population is marked as XC _C2 , the third capacitor population is marked as XC _C3 , the fourth capacitor population is marked as XC _C4 , and the fifth capacitor population is marked as XC _C5 ; all capacitor populations in the equivalent circuit Expressed as XC _a ={XC _C1 , XC _C2 , XC _C3 , XC _C4 , XC _C5 } in a set form;

XL _b represents the inductance population, and b represents the identity of the inductance in the equivalent circuit. For example, the first inductance population of the first inductance L1 is denoted as XL _L1 ; similarly, the second inductance population is denoted as XL _L2 , and the third inductance population is denoted as XL L2 . is XL _L3 , the fourth inductance population is denoted as XL _L4 , the fifth inductance population is denoted as XL _L5 ; all inductance populations in the equivalent circuit are represented as XL _b = {XL _L1 , XL _L2 , XL _L3 , XL _L4 , XL _L5 };

XR _d represents the resistance population, and d represents the identification of the resistance in the equivalent circuit. For example, the first resistance population of the first resistance R1 is marked as XR _R1 ; similarly, the second resistance population is marked as XR _R2 , and the third resistance population is marked as XR R2 . is XR _R3 ; all resistance populations in the equivalent circuit are expressed as XR _d ={XR _R1 , XR _R2 , XR _R3 } in a collective form;

XT _e represents the transformer population, and e represents the input/output voltage ratio of the transformer in the equivalent circuit. The input/output voltage ratio of the transformer T0 used in the invention is DT:1.

In the present invention, the variable X={XC _a , XL _b , XR _d , XT _e } to be optimized can also be expressed as $x=\left\{\begin{array}{ccccc}{\mathrm{XC}}_{C1},& {\mathrm{<figure-callout\; id="2"\; label="XC\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">XC</figure-callout>}}_C,& {\mathrm{XC}}_{C3},& {\mathrm{<figure-callout\; id="4"\; label="XC\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">XC</figure-callout>}}_C,& {\mathrm{XC}}_{C5}\\ {\mathrm{XL}}_{L1},& {\mathrm{<figure-callout\; id="2"\; label="XL\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">XL</figure-callout>}}_L,& {\mathrm{XL}}_{L3},& {\mathrm{<figure-callout\; id="4"\; label="XL\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">XL</figure-callout>}}_L,& {\mathrm{XL}}_{L5}\\ {\mathrm{XR}}_{R1},& {\mathrm{<figure-callout\; id="2"\; label="XR\; R"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">XR</figure-callout>}}_R,& {\mathrm{XR}}_{R3}& & \\ {\mathrm{XT}}_e& & & & \end{array}\right\}.$

Step 2: Chromosome processing based on genetic algorithm to obtain the total population

0＜DC≤800pF。

表示标识a电容种群在第1个染色体中的变量值，表示标识a电容种群在第2个染色体中的变量值，……，

表示标识a电容种群在第m个染色体中的变量值，也称标识a电容种群在任意一个染色体中的变量值。In step two, based on the chromosomes in the genetic algorithm, randomly generate m variable values in the variable value DC for the capacitance population XC _a

0<DC≤800pF.

Indicates the variable value that identifies the a capacitance population in the first chromosome, Indicates the variable value that identifies the a capacitance population in the second chromosome, ...,

Indicates the variable value that identifies the a-capacitance population in the mth chromosome, and is also called the variable value that identifies the a-capacitance population in any chromosome.

0＜DL≤0.1μH。

表示标识b电感种群在第1个染色体中的变量值，

表示标识b电感种群在第2个染色体中的变量值，……，

表示标识b电感种群在第w个染色体中的变量值，也称标识b电感种群在任意一个染色体中的变量值。Based on the chromosome in the genetic algorithm, randomly generate w variable values in the variable value DL for the inductance population XL _b

0<DL≤0.1μH.

Indicates the variable value identifying the b inductance population in the first chromosome,

Indicates the variable value that identifies the b inductance population in the second chromosome, ...,

Indicates the variable value that identifies the b-inductance population in the wth chromosome, and is also called the variable value that identifies the b-inductance population in any chromosome.

0＜DR≤5kΩ。

表示标识d电阻种群在第1个染色体中的变量值，

表示标识d电阻种群在第2个染色体中的变量值，……，

表示标识d电阻种群在第v个染色体中的变量值，也称标识d电阻种群在任意一个染色体中的变量值。Based on the chromosome in the genetic algorithm, randomly generate v variable values in the variable value DR for the resistance population XR _d

0<DR≤5kΩ.

Indicates the variable value identifying the d resistance population in the first chromosome,

Indicates the variable value that identifies the d resistance population in the second chromosome, ...,

Indicates the variable value that identifies the d-resistance population in the vth chromosome, and is also called the variable value that identifies the d-resistance population in any chromosome.

0.1≤DT≤10。

表示标识e变压器种群在第1个染色体中的变量值，

表示标识e变压器种群在第2个染色体中的变量值，……，

表示标识e变压器种群在第n个染色体中的变量值，也称标识e变压器种群在任意一个染色体中的变量值。Based on the chromosome in the genetic algorithm, randomly generate n variable values in the variable value DT for the transformer population XT _e

0.1≤DT≤10.

Indicates the variable value identifying the e-transformer population in the first chromosome,

Indicates the variable value identifying the e-transformer population in the second chromosome, ...,

Indicates the variable value that identifies the e-transformer population in the nth chromosome, and is also called the variable value that identifies the e-transformer population in any chromosome.

In the present invention, the variables m, w, v and n of the chromosome take 200 values. All values in the variable group are coded separately, and the variable group is transformed into a chromosome, and one code is called an individual in the chromosome. For the variable X={XC _a , XL _b , XR _d , XT _e } to be optimized, the total population is obtained by the chromosome processing in the genetic algorithm

Step 3: use the multi-objective optimization function to allocate populations for each function in the objective function according to the parallel selection method;

中的全部个体(染色体)按目标函数M_目标＝{f_目标，l_目标}的个数均等地划分为两个子群体Q₁和Q₂(子群体Q₁也称为第一子群体，子群体Q₂也称为第二子群体)，对每个子群体分配目标函数M_目标＝{f_目标，l_目标}中的一个进行优化。在本发明中，若第一子群体Q₁采用了目标函数M_目标＝{f_目标，l_目标}中的天线驻波比f_目标进行优化，则第二子群体Q₂应当采用目标函数M_目标＝{f_目标，l_目标}中的转换功率增益l_目标进行优化；反之，若第二子群体Q₂采用了目标函数M_目标＝{f_目标，l_目标}中的天线驻波比f_目标标进行优化，则第一子群体Q₁应当采用目标函数M_目标＝{f_目标，l_目标}中的转换功率增益l_目标进行优化。In step three, the total population

All individuals (chromosomes) in the target function M _target ={f _target , l _target } are equally divided into two subgroups _Q1 and _Q2 (subgroup _Q1 is also called the first subgroup, subgroup Q ₂ is also called the second subgroup), and each subgroup is assigned one of the objective functions _Mobjective ={ _fobjective , _lobjective } for optimization. In the present invention, if the first subgroup _Q1 adopts the antenna standing wave ratio f _target in the objective function M _target ={f _target , l _target } to optimize, then the second subgroup _Q2 should adopt the objective function M _target ={f _target , l _target in the conversion power gain l _target is optimized; On the contrary, if the second subgroup _Q2 has adopted the antenna standing wave ratio f target in the objective function _{M target} ={f _target , l _target } For optimization, the first subgroup Q ₁ should be optimized using the conversion power gain l _objective in the objective function M _objective = {f _objective , l _objective }.

Step 4: Obtain the optimized amount of the subpopulation by crossover mutation;

In step 4, carry out cross-mutation on the first subpopulation Q ₁ , and retain the optimization amount DQ ₁ of each generation (also called the first optimization amount DQ ₁ ); carry out cross-mutation on the second sub-population Q ₂ , and retain the optimization amount DQ 1 of each generation. Quantity DQ ₂ (also known as the second optimization quantity DQ ₂ ); the specific steps for obtaining the optimization quantity of each generation through cross mutation are:

作为当前染色体

也称为当前第一染色体

Step 401: Obtain any 2 chromosomes in the first subgroup Q ₁

as the current chromosome

also known as the current first chromosome

作为当前染色体

也称为当前第二染色体 Get any 2 chromosomes in the second subpopulation Q ₂

as the current chromosome

also known as the current second chromosome

中的两个个体进行交叉处理，生成新第一染色体

表示交叉后第一个染色体，

表示交叉后第二个染色体；所述交叉处理依据第一适应性策略模型 $P_{c1}=\left\{\begin{array}{cc}(f_{\min }-f_{\mathrm{avg}})/(f_{\min }-f),& f\&le;f_{\mathrm{avg}}\\ 1.0,& f>f_{\mathrm{avg}}\end{array}\right.$ 进行的；P_c1表示第一子群体Q₁的交叉概率(也称为第一交叉概率)，f_min表示第一子群体Q₁中最佳个体适应度值，f表示为要交叉的两个个体中较适应的适应值，且f＝min{f₁，f₂}，f₁表示染色体

对应的驻波比优化目标f_目标的值，f₂表示染色体

对应的驻波比优化目标f_目标的值，f_avg表示第一子群体Q₁的平均适应度值；Step 402: For the current first chromosome

The two individuals in are crossed over to generate a new first chromosome

Indicates the first chromosome after crossover,

Indicates the second chromosome after crossover; the crossover process is based on the first adaptive strategy model $P_{c1}=\left\{\begin{array}{cc}(f_{\min }-f_{\mathrm{avg}})/(f_{\min }-f),& f\&le;f_{\mathrm{avg}}\\ 1.0,& f>f_{\mathrm{avg}}\end{array}\right.$ carried out; P _c1 represents the crossover probability of the first subgroup Q ₁ (also called the first crossover probability), f _min represents the best individual fitness value in the first subgroup _Q1 , and f represents the two The more adaptive fitness value in the individual, and f=min{f ₁ , f ₂ }, f ₁ represents the chromosome

The value of the corresponding SWR optimization target f _target , f ₂ represents the chromosome

The value of the corresponding standing wave ratio optimization target f _target , f _avg represents the average fitness value of the first subgroup _Q1 ;

中的两个个体进行交叉处理，生成新第二染色体

表示交叉后第三个染色体，

表示交叉后第四个染色体；所述交叉处理依据第二适应性策略模型 $P_{c2}=\left\{\begin{array}{cc}(l_{\max }-l)/(l_{\max }-l_{\mathrm{avg}}),& l\&GreaterEqual;l_{\mathrm{avg}}\\ 1.0,& l<l_{\mathrm{avg}}\end{array}\right.$ 进行的；P_c2表示第二子群体Q₂的交叉概率，也称为第二交叉概率，l_max表示第二子群体Q₂中最佳个体适应度值，l表示为要交叉的两个个体中较适应的适应值，且l＝max{l₁，l₂}，l₁表示染色体

对应的功率优化目标l_目标的值，l₂表示染色体

对应的功率优化目标l_目标的值，l_avg表示第二子群体Q₂的平均适应度值；to the current second chromosome

Two individuals in are crossed over to generate a new second chromosome

Indicates the third chromosome after crossover,

Indicates the fourth chromosome after crossover; the crossover process is based on the second adaptive strategy model $P_c$$2_{}=\left\{\begin{array}{cc}(l_{\max }-l)/(l_{\max }-l_{\mathrm{avg}}),& l\&Greater\; Equal;l_{\mathrm{avg}}\\ 1.0,& l<l_{\mathrm{avg}}\end{array}\right.$ carried out; P _c2 represents the crossover probability of the second subgroup _Q2 , also known as the second crossover probability, l _max represents the best individual fitness value in the second subgroup _Q2 , and l represents the two individuals to be crossover The more adaptive fitness value in , and l=max{l ₁ , l ₂ }, l ₁ represents the chromosome

The value of the corresponding power optimization target l _target , l ₂ represents the chromosome

The value of the corresponding power optimization target l _target , l _avg represents the average fitness value of the second subgroup _Q2 ;

对应的驻波比优化目标f_目标的值，f₄表示交叉后第二个染色体

对应的驻波比优化目标f_目标的值；Step 403: Compare f ₁ and f ₃ and f ₂ and f ₄ , if f ₁ ≥ f ₃ and f ₂ ≥ f ₄ , use AQ _cross instead of AQ _current ; if f ₁ < f ₃ or f ₂ < f ₄ , then AQ is _currently unchanged; f ₃ means the first chromosome after crossover

The value of the corresponding VSWR optimization target f _target , f ₄ means the second chromosome after crossover

The value of the corresponding SWR optimization target f _target ;

对应的功率优化目标l_目标的值，l₄表示交叉后第四染色体

对应的功率优化目标l_目标的值；Compare l ₁ and l ₃ and l ₂ and l ₄ , if l ₁ ≤ _{l 3} and l ₂ ≤ l ₄ , use BQ _cross instead of BQ _current ; if l ₁ > l ₃ or l ₂ > l ₄ , then BQ _Currently unchanged; l ₃ means the third chromosome after crossover

The value of the corresponding power optimization target l _target , l ₄ means the fourth chromosome after crossover

The value of the corresponding power optimization target l _target ;

中的两个个体分别进行变异处理，生成变异第一染色体

表示变异后的染色体，

表示

变异后的染色体；所述的变异处理依据第三适应性策略模型 $P_{m1}=\left\{\begin{array}{cc}0.5(f_{\min }-f_{\mathrm{avg}})/(f_{\min }-f^{\&prime;}),& f^{\&prime;}\&le;f_{\mathrm{avg}}\\ 0.5,& f^{\&prime;}>f_{\mathrm{avg}}\end{array}\right.$ 进行的；P_m1表示第一子群体Q₁的变异概率(也称为第一变异概率)，f_min表示第一子群体Q₁中最佳个体适应度值，f_avg表示第一子群体Q₁的平均适应度值，f′为需要变异个体的适应度值，且f₁表示染色体对应的驻波比优化目标f_目标的值，f₂表示染色体

对应的驻波比优化目标f_目标的值；Step 404: For the current first chromosome

The two individuals in are mutated separately to generate the mutated first chromosome

express mutated chromosomes,

express

Chromosomes after mutation; the mutation processing is based on the third adaptive strategy model $P_{m1}=\left\{\begin{array}{cc}0.5(f_{\min }-f_{\mathrm{avg}})/(f_{\min }-f^{\&prime;}),& f^{\&prime;}\&le;f_{\mathrm{avg}}\\ 0.5,& f^{\&prime;}>f_{\mathrm{avg}}\end{array}\right.$ carried out; P _m1 represents the mutation probability of the first subgroup Q ₁ (also known as the first mutation probability), f _min represents the best individual fitness value in the first subgroup _Q1 , and f _avg represents the first subgroup Q The average fitness value of ₁ , f' is the fitness value of the individual that needs to be mutated, and f ₁ means chromosome The value of the corresponding SWR optimization target f _target , f ₂ represents the chromosome

The value of the corresponding SWR optimization target f _target ;

中的两个个体分别进行变异处理，生成变异第二染色体

表示

变异后的染色体，

表示

变异后的染色体；所述变异处理依据第四适应性策略模型 $P_{m2}=\left\{\begin{array}{cc}0.5(l_{\max }-l^{\&prime;})/(l_{\max }-l_{\mathrm{avg}}),& l^{\&prime;}\&GreaterEqual;l_{\mathrm{avg}}\\ 0.5,& l^{\&prime;}<l_{\mathrm{avg}}\end{array}\right.$ 进行的；P_m2表示第二子群体Q₂的变异概率，也称为第二变异概率，l_max表示第二子群体Q₂中最佳个体适应度值，l_avg表示第二子群体Q₂的平均适应度值，l′为要变异个体的适应度值且

l₁表示染色体

对应的功率优化目标l_目标的值，l₂表示染色体

对应的功率优化目标l_目标的值；to the current second chromosome

The two individuals in are mutated separately to generate the mutated second chromosome

express

mutated chromosomes,

express

Chromosomes after mutation; the mutation processing is based on the fourth adaptive strategy model $P_m$$2_{}=\left\{\begin{array}{cc}0.5(l_{\max }-l^{\&prime;})/(l_{\max }-l_{\mathrm{avg}}),& l^{\&prime;}\&Greater\; Equal;l_{\mathrm{avg}}\\ 0.5,& l^{\&prime;}<l_{\mathrm{avg}}\end{array}\right.$ carried out; P _m2 represents the mutation probability of the second subgroup _Q2 , also known as the second mutation probability, l _max represents the best individual fitness value in the second subgroup _Q2 , and l _avg represents the second subgroup _Q2 The average fitness value of , l' is the fitness value of the individual to be mutated and

l ₁ means chromosome

The value of the corresponding power optimization target l _target , l ₂ represents the chromosome

The value of the corresponding power optimization target l _target ;

代替

若f₁≤f₅时，则

不变；f₅表示

变异后的染色体

对应的驻波比优化目标f_目标的值；Step 405: compare f ₁ and f ₅ , if f ₁ > f ₅ , use

replace

If f ₁ ≤ f ₅ , then

No change; f ₅ means

mutated chromosome

The value of the corresponding SWR optimization target f _target ;

代替

若f₂≤f₆时，则

不变；f₆表示变异后的染色体

对应的驻波比优化目标f_目标的值；Compare f ₂ and f ₆ , if f ₂ > f ₆ , use

replace

If f ₂ ≤ f ₆ , then

No change; f ₆ means mutated chromosome

The value of the corresponding SWR optimization target f _target ;

代替若l₁≥l₅时，则

不变；l₅表示

变异后的染色体

对应的功率优化目标l_目标的值；Compare l ₁ and l ₅ , if l ₁ < l ₅ , use

replace If l ₁ ≥ _{l 5} , then

unchanged; l ₅ means

mutated chromosome

The value of the corresponding power optimization target l _target ;

若l₂≥l₆时，则

不变；l₆表示变异后的染色体

对应的功率优化目标l_目标的值；Compare l ₂ and l ₆ , if l ₂ <l ₆ , use replace

If l ₂ ≥ _{l 6} , then

unchanged; l ₆ means mutated chromosome

The value of the corresponding power optimization target l _target ;

Repeat steps 401 to 405 until all the cross-mutation of chromosomes in the first subgroup _Q1 and the second subgroup _Q2 is completed, and the optimal optimization quantity of the first subgroup _Q1 of the current generation is obtained, that is, the first optimization quantity _DQ1 , the optimal optimization quantity of the second subgroup Q ₂ , that is, the second optimization quantity DQ ₂ .

Step 5: According to the objective function M _objective = {f _objective , l _objective }, traverse all chromosomes in the optimized total population Q _total ' to obtain the current generation optimal individual in the genetic algorithm;

并把

赋给I_hbest，以便下一代最优个体与当前代的最优个体进行比较，在两者中选择出较优个体，并且赋给I_hbest。In step 5, the first optimized quantity DQ ₁ and the second optimized quantity DQ ₂ are combined to form a new population, that is, the optimized total population Q _total ', and the total optimized population Q is traversed according to the objective function M _objective = {f _objective , l _objective } All the chromosomes in _{the total} ' get the optimal individual of the current generation in the genetic algorithm

and put

It is assigned to I _hbest , so that the optimal individual of the next generation can be compared with the optimal individual of the current generation, and a better individual is selected from the two, and assigned to I _hbest .

Judging whether the termination condition of the genetic algorithm is reached, if the termination condition of the genetic algorithm is not satisfied, then return to step 4, if the termination condition of the genetic algorithm is met, then the optimal individual I _hbest in the genetic algorithm is obtained, and retained, and then step 6 is entered; The termination condition of the genetic algorithm refers to whether the number of iteration steps K is 0. If the number of iteration steps K is not 0, return to step 3. If the number of iteration steps K is 0, the optimal individual I _hbest in the genetic algorithm is obtained. And keep it, go to step six; in order to correspond to the expression form of the variable X={XC _a , XL _b , XR _d , XT _e } to be optimized, the expression form of the optimal solution I _hbest set of electronic component parameters is $I_{\mathrm{hbest}}=\left[\begin{array}{ccccc}{C}_{C1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^2,& C_{C3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^4,& C_{C5}^{\mathrm{hbest}}\\ L_{L1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^2,& L_{L3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^4,& L_{L5}^{\mathrm{hbest}}\\ R_{R1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="R\; R"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">R</figure-callout>}}_R^2,& R_{R3}^{\mathrm{hbest}}& & \\ T_e^{\mathrm{hbest}}& & & & \end{array}\right].$

表示第一电容种群XC_C1经遗传算法后的最优电容值；

Indicates the optimal capacitance value of the first capacitance population XC _C1 after genetic algorithm;

表示第二电容种群XC_C2经遗传算法后的最优电容值；

Indicates the optimal capacitance value of the second capacitance population XC _C2 after genetic algorithm;

Indicates the optimal capacitance value of the third capacitor population XC _C3 after genetic algorithm;

Indicates the optimal capacitance value of the fourth capacitance population XC _C4 after genetic algorithm;

表示第五电容种群XC_C5经遗传算法后的最优电容值；

Indicates the optimal capacitance value of the fifth capacitor population XC _C5 after genetic algorithm;

表示第一电感种群XL_L1经遗传算法后的最优电感值；

Indicates the optimal inductance value of the first inductance population XL _L1 after genetic algorithm;

表示第二电感种群XL_L2经遗传算法后的最优电感值；

Indicates the optimal inductance value of the second inductance population XL _L2 after genetic algorithm;

表示第三电感种群XL_L3经遗传算法后的最优电感值；

Indicates the optimal inductance value of the third inductance population XL _L3 after genetic algorithm;

表示第四电感种群XL_L4经遗传算法后的最优电感值；

Indicates the optimal inductance value of the fourth inductance population XL _L4 after genetic algorithm;

表示第五电感种群XL_L5经遗传算法后的最优电感值；

Indicates the optimal inductance value of the fifth inductance population XL _L5 after genetic algorithm;

Indicates the optimal electrical group value of the first resistance population XR _R1 after genetic algorithm;

表示第二电阻种群XR_R2经遗传算法后的最优电组值；

Indicates the optimal electrical group value of the second resistance population XR _R2 after genetic algorithm;

表示第三电阻种群XR_R3经遗传算法后的最优电组值；

Indicates the optimal electrical group value of the third resistance population XR _R3 after genetic algorithm;

表示变压器种群XT_e经遗传算法后的最优变压器的输入/输出电压比值。

Indicates the input/output voltage ratio of the optimal transformer of the transformer population XT _e after the genetic algorithm.

Step 6: Perform an initial annealing assignment on the best individual I _hbest of the current generation to obtain _the initial individual I;

In the present invention, for $I_{\mathrm{hbest}}=\left[\begin{array}{ccccc}{C}_{C1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^2,& C_{C3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^4,& C_{C5}^{\mathrm{hbest}}\\ L_{L1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^2,& L_{L3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^4,& L_{L5}^{\mathrm{hbest}}\\ R_{R1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="R\; R"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">R</figure-callout>}}_R^2,& R_{R3}^{\mathrm{hbest}}& & \\ T_e^{\mathrm{hbest}}& & & & \end{array}\right]$ Perform initial annealing assignment, then the initial individual with simulated annealing algorithm is

表示第一电容种群XC_C1在模拟退火算法中设置的初始值，且

Represents the initial value of the first capacitance population XC _C1 set in the simulated annealing algorithm, and

表示第二电容种群XC_C2在模拟退火算法中设置的初始值，且

Represents the initial value of the second capacitance population XC _C2 set in the simulated annealing algorithm, and

表示第三电容种群XC_C3在模拟退火算法中设置的初始值，且

Represents the initial value of the third capacitance population XC _C3 set in the simulated annealing algorithm, and

表示第四电容种群XC_C4在模拟退火算法中设置的初始值，且

Represents the initial value of the fourth capacitance population XC _C4 set in the simulated annealing algorithm, and

表示第五电容种群XC_C5在模拟退火算法中设置的初始值，且

Represents the initial value of the fifth capacitance population XC _C5 set in the simulated annealing algorithm, and

表示第一电感种群XL_L1在模拟退火算法中设置的初始值，且

Represents the initial value of the first inductance population XL _L1 set in the simulated annealing algorithm, and

Represents the initial value of the second inductance population XL _L2 set in the simulated annealing algorithm, and

表示第三电感种群XL_L3在模拟退火算法中设置的初始值，且

Represents the initial value of the third inductance population XL _L3 set in the simulated annealing algorithm, and

表示第四电感种群XL_L4在模拟退火算法中设置的初始值，且

Represents the initial value of the fourth inductance population XL _L4 set in the simulated annealing algorithm, and

表示第五电感种群XL_L5在模拟退火算法中设置的初始值，且

Indicates the initial value of the fifth inductance population XL _L5 set in the simulated annealing algorithm, and

表示第一电阻种群XR_R1在模拟退火算法中设置的初始值，且

Represents the initial value of the first resistance population XR _R1 set in the simulated annealing algorithm, and

Represents the initial value of the second resistance population XR _R2 set in the simulated annealing algorithm, and

表示第三电阻种群XR_R3在模拟退火算法中设置的初始值，且

Represents the initial value of the third resistance population XR _R3 set in the simulated annealing algorithm, and

表示变压器种群XT_e在模拟退火算法中设置的初始值，且

Represents the initial value of the transformer population XT _e set in the simulated annealing algorithm, and

Step 7: Assign a trial value to the best individual I _hbest of the current generation to obtain the trial individual I _trial ;

进行试探赋值，则有新试探值个体

In the present invention, the initial individual of the simulated annealing algorithm

If tentative assignment is performed, there will be a new tentative value individual

是第一电容种群XC_C1在变量取值范围为Δx_C1内随机生成的，

is randomly generated by the first capacitor population XC _C1 within the variable value range of Δx _C1 ,

is randomly generated by the second capacitor population XC _C2 within the variable value range of Δx _C2 ,

是第三电容种群XC_C3在变量取值范围为Δx_C3内随机生成的，

is randomly generated by the third capacitor population XC _C3 within the variable value range of Δx _C3 ,

is randomly generated by the fourth capacitor population XC _C4 within the variable value range of Δx _C4 ,

是第五电容种群XC_C5在变量取值范围为Δx_C5内随机生成的，

is randomly generated by the fifth capacitor population XC _C5 within the variable value range of Δx _C5 ,

是第一电感种群XL_L1在变量取值范围为Δx_L1内随机生成的，

is randomly generated by the first inductance population XL _L1 within the variable value range of Δx _L1 ,

is randomly generated by the second inductance population XL _L2 within the variable value range of Δx _L2 ,

是第三电感种群XL_L3在变量取值范围为Δx_L3内随机生成的，

is randomly generated by the third inductance population XL _L3 within the variable value range of Δx _L3 ,

is randomly generated by the fourth inductance population XL _L4 within the variable value range of Δx _L4 ,

是第五电感种群XL_L5在变量取值范围为Δx_L5内随机生成的，

is randomly generated by the fifth inductance population XL _L5 within the variable value range of Δx _L5 ,

是第一电阻种群XR_R1在变量取值范围为Δx_R1内随机生成的，

is randomly generated by the first resistance population XR _R1 within the variable value range of Δx _R1 ,

是第二电阻种群XR_R2在变量取值范围为Δx_R2内随机生成的，

is randomly generated by the second resistance population XR _R2 within the variable value range of Δx _R2 ,

是第三电阻种群XR_R3在变量取值范围为Δx_R3内随机生成的，

is randomly generated by the third resistance population XR _R3 within the variable value range of Δx _R3 ,

是变压器种群XT_e在变量取值范围为Δx_Te内随机生成的，

is randomly generated by the transformer population XT _e within the variable value range Δx _Te ,

Step 8: Perform simulated annealing optimization according to the objective function M _target ={f _target , l _target }, the initial individual I _initial and the trial individual I _trial to obtain the optimized solution of the electronic component parameters;

对应的目标函数M_目标＝{f_目标，l_目标}的值分别为ff₁和ll₁，ff₁表示试探个体I_试探对应的驻波比优化目标f_目标的值，ll₁表示试探个体I_试探对应的功率优化目标l_目标的值；Step 801: Calculate the trial individual

The values of the corresponding objective function M _target = {f _target , l _target } are respectively ff ₁ and ll ₁ , ff ₁ represents the value of the SWR optimization target f _target corresponding to the trial individual I _trial , and ll ₁ represents the value of the trial individual I _trial The value of the corresponding power optimization target l _target ;

对应的目标函数值M_目标＝{f_目标，l_目标}的值分别为ff₂和ll₂，ff₂表示初始个体I_初始对应的驻波比优化目标f_目标的值，ll₂表示初始个体I_初始对应的功率优化目标l_目标的值；Step 802: Calculate the initial individual

The values of the corresponding objective function value M _target = {f _target , l _target } are ff ₂ and ll ₂ respectively, ff ₂ represents the value of _initial individual I corresponding to the SWR optimization target f _target , ll ₂ represents the initial individual I The value of the _initial corresponding power optimization target l _target ;

Step 803: Judging whether the values ff ₁ and ll ₁ of the objective function value _Mtarget ={ _ftarget , _ltarget } of the _trial individual I are better than _{the initial} corresponding objective function value _Mtarget ={ _ftarget , _ltarget } values ff ₂ and ll ₂ , if ff ₁ ≤ ff ₂ and ll ₁ ≥ ll ₂ , enter step 804, otherwise return to step 801;

Step 804: Calculate whether the trial individual I _trial satisfies the receiver function relationship $\left[\begin{array}{c}{P}_{\mathrm{VSWR}}=\exp \left(\frac{{\mathrm{ff}}_1-{\mathrm{ff}}_2}{T_{\mathrm{now}}}\right)>r\&Element;\left[\mathrm{0,1}\right]\\ P_G=\exp \left(\frac{{\mathrm{ll}}_2-{\mathrm{ll}}_1}{T_{\mathrm{now}}}\right)>r\&Element;\left[\mathrm{0,1}\right]\end{array}\right.,$ If it is satisfied, the trial individual I _will replace the _initial individual I, and enter step 805; if not, return to step 801; P _VSWR represents the receiving probability corresponding to the standing wave ratio, _PG represents the receiving probability corresponding to the conversion gain, and T _now represents The current temperature, r is a random number randomly generated with the probability of a uniform distribution function in the range [0, 1], that is, r = RAN(0, 1)

Step 805: In the annealing algorithm, the temperature is lowered at the cooling speed a, and it is judged whether the current temperature T _now is less than the cut-off temperature T _end , if T _now is greater than T _end , return to step 801; if T _now is less than or equal to T _end , then set The optimized initial individual I _{is initially used} as the electronic component parameter optimization solution I _best after genetic-simulated annealing treatment, $I_{\mathrm{hbest}}=\left[\begin{array}{ccccc}{C}_{C1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^2,& C_{C3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^4,& C_{C5}^{\mathrm{hbest}}\\ L_{L1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^2,& L_{L3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^4,& L_{L5}^{\mathrm{hbest}}\\ R_{R1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="R\; R"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">R</figure-callout>}}_R^2,& R_{R3}^{\mathrm{hbest}}& & \\ T_e^{\mathrm{hbest}}& & & & \end{array}\right].$

The realization of both genetic algorithm and simulated annealing algorithm needs to select several control parameters reasonably. The control parameters of the present invention are selected as follows:

Genetic algorithm part: the number of iteration steps K is 250.

Simulated annealing algorithm part: the initial temperature T ₀ is 0.05 degrees, the cut-off temperature T _end is 0.00001 degrees, and the cooling rate a is 0.9.

Matlab software is used to simulate the parameters of the electronic components in the matching network (as shown in Figure 2) by genetic-simulated annealing combination algorithm, and the optimized parameters are shown in Table 1.

Table 1 The matching network component values obtained by the optimization of genetic-simulated annealing combined algorithm

component component value component component value T0 (DT: 1) 0.88 R1 7.2Ω R2 955Ω R3 900Ω C1 23pF L1 1.8pH

C2 9pF L2 18nH C3 7.8pF L3 10nH C4 0pF L4 25nH C5 15pF L5 1.1pH

Because the component values of L1, C4, and L5 in the optimized matching network are very small, and their contribution to the matching network is not large, these components can be removed. The structure diagram of the simplified antenna broadband matching network is shown in Fig. 4 . In Fig. 4, the connection end of the antenna in the equivalent circuit of the broadband matching network is connected to the 1 end of the transformer T0; the 2 ends of the transformer T0 are grounded; the 1 end of the coaxial cable is connected to the end of the first resistor R1, and the coaxial cable 2 ends of the coaxial cable are grounded, and a third resistor R3 is connected in parallel between 1 end and 2 ends of the coaxial cable;

Terminal 3 of the transformer T0 is connected to terminal 4 of the transformer T0 through the first capacitor C1 and the second capacitor C2;

Terminal 3 of the transformer T0 is connected to terminal 4 of the transformer T0 through the first capacitor C1 and the second inductance L2;

Terminal 3 of the transformer T0 is connected to terminal 4 of the transformer T0 through the first capacitor C1, the third capacitor C3, the third inductor L3, and the fourth inductor L4;

Terminal 3 of the transformer T0 is connected to terminal 4 of the transformer T0 through the first capacitor C1, the third capacitor C3, the third inductor L3, the fifth capacitor C5, and the second resistor R2;

Terminal 3 of the transformer T0 is connected to terminal 4 of the transformer T0 via the first capacitor C1 , the third capacitor C3 , the third inductor L3 , the fifth capacitor C5 , the first resistor R1 and the third resistor R3 .

In the present invention, between the secondary side of the transformer T0 and the coaxial cable access end, the antenna impedance is matched by using a multi-stage filter (consisting of two T-shaped L-C networks), so that the broadband matching network equivalent circuit The structure is reasonable.

Claims (6)

1. a method for optimizing the parameters of electronic components in the antenna broadband matching network using genetic-simulated annealing combination, is characterized in that comprising the following steps: Step 1: Initialize the population based on the genetic algorithm, and obtain the variables to be optimized X={XC _a , XL _b , XR _d , XT _e }; In step 1, the capacitance, inductance and resistance in the equivalent circuit of the broadband matching network are processed by the population based on the genetic algorithm, and the variables to be optimized X={XC _a , XL _b , XR _d , XT _e } are obtained; In the variable X={XC _a , XL _b , XR _d , XT _e } to be optimized, XC _a represents the capacitance population, and a represents the identification of the capacitance in the equivalent circuit, such as the first capacitance population of the first capacitance C1 is denoted as XC _C1 ; similarly, the second capacitor population is marked as XC _C2 , the third capacitor population is marked as XC _C3 , the fourth capacitor population is marked as XC _C4 , and the fifth capacitor population is marked as XCC _C5 ; all capacitor populations in the equivalent circuit Expressed as XC _a = {XC _C1 , XC _C2 , XC _C3 , XC _C4 , XC _C5 } in a set form; XL _b represents the inductance population, and b represents the identity of the inductance in the equivalent circuit. For example, the first inductance population of the first inductance L1 is denoted as XL _L1 ; similarly, the second inductance population is denoted as XL _L2 , and the third inductance population is denoted as XL L2 . is XL _L3 , the fourth inductance population is denoted as XL _L4 , the fifth inductance population is denoted as XL _L5 ; all inductance populations in the equivalent circuit are represented as XL _b = {XL _L1 , XL _L2 , XL _L3 , XL _L4 , XL _L5 }; XR _d represents the resistance population, and d represents the identification of the resistance in the equivalent circuit. For example, the first resistance population of the first resistance R1 is marked as XR _R1 ; similarly, the second resistance population is marked as XR _R2 , and the third resistance population is marked as XR R2 . is XR _R3 ; all resistance populations in the equivalent circuit are expressed as XR _d ={XR _R1 , XR _R2 , XR _R3 } in a collective form; XT _e represents the transformer population, and e represents the input/output voltage ratio of the transformer in the equivalent circuit; Step 2: Chromosome processing based on genetic algorithm to obtain the total population

In step two, based on the chromosomes in the genetic algorithm, randomly generate m variable values in the variable value DC for the capacitance population XC _a ${\mathrm{DXC}}_a^m=\{{\mathrm{XC}}_a^1,{\mathrm{XC}}_a^2,\&CenterDot;\&CenterDot;\&CenterDot;,{\mathrm{XC}}_a^m\},$ 0<DC≤800pF;

Indicates the variable value that identifies the a capacitance population in the first chromosome,

Indicates the variable value that identifies the a capacitance population in the second chromosome, ...,

Indicates the variable value that identifies the a-capacitance population in the mth chromosome, also known as the variable value that identifies the a-capacitance population in any chromosome; Based on the chromosome in the genetic algorithm, randomly generate w variable values in the variable value DL for the inductance population XL _b

0<DL≤0.1μH;

Indicates the variable value identifying the b inductance population in the first chromosome,

Indicates the variable value that identifies the b inductance population in the second chromosome, ...,

Indicates the variable value that identifies the b inductance population in the wth chromosome, also known as the variable value that identifies the b inductance population in any chromosome; Based on the chromosome in the genetic algorithm, randomly generate v variable values in the variable value DR for the resistance population XR _d

0<DR≤5kΩ;

Indicates the variable value identifying the d resistance population in the first chromosome,

Indicates the variable value that identifies the d resistance population in the second chromosome, ..., Indicates the variable value that identifies the d resistance population in the vth chromosome, also known as the variable value that identifies the d resistance population in any chromosome; Based on the chromosome in the genetic algorithm, randomly generate n variable values in the variable value DT for the transformer population XT _e

0.1≤DT≤10;

Indicates the variable value identifying the e-transformer population in the first chromosome,

Indicates the variable value identifying the e-transformer population in the second chromosome, ..., Indicates the variable value identifying the e-transformer population in the nth chromosome, also known as the variable value identifying the e-transformer population in any chromosome; For the variable X={XC _a , XL _b , XR _d , XT _e } to be optimized, the total population is obtained by the chromosome processing in the genetic algorithm

Step 3: use the multi-objective optimization function to allocate populations for each function in the objective function according to the parallel selection method; In step three, the total population

Chromosomes in are equally divided into the first subgroup Q ₁ and the second subgroup Q ₂ according to the number of the objective function _Mobjective ={ _fobjective , _lobjective }, and each subgroup is assigned the objective function _Mobjective ={ _fobjective , one of _lobjective } to optimize; Step 4: Obtain the optimized amount of the subpopulation by crossover mutation; In step 4, cross-mutation is performed on the first subgroup _Q1 , and the optimized amount of each generation is retained, that is, the first optimized amount _DQ1 ; cross-mutation is performed on the second subgroup _Q2 , and the optimized amount of each generation is retained, that is, the second optimized amount The optimization quantity DQ ₂ ; the specific steps for cross-mutation to obtain the optimization quantity of each generation are: Step 401: Obtain any 2 chromosomes in the first subgroup Q ₁

as the current chromosome

also known as the current first chromosome

Get any 2 chromosomes in the second subpopulation Q ₂

as the current chromosome

also known as the current second chromosome

Step 402: For the current first chromosome

The two individuals in are crossed over to generate a new first chromosome

Indicates the first chromosome after crossover,

Indicates the second chromosome after crossover; the crossover process is based on the first adaptive strategy model $P_{c1}=\left\{\begin{array}{cc}(f_{\min }-f_{\mathrm{avg}})/(f_{\min }-f),& f\&le;f_{\mathrm{avg}}\\ 1.0,& f>f_{\mathrm{avg}}\end{array}\right.$ carried out; P _c1 represents the crossover probability of the first subgroup Q ₁ (also called the first crossover probability), f _min represents the best individual fitness value in the first subgroup _Q1 , and f represents the two The more adaptive fitness value in the individual, and f=min{f ₁ ,f ₂ }, f ₁ represents the chromosome

The value of the corresponding SWR optimization target f _target , f ₂ represents the chromosome

The value of the corresponding standing wave ratio optimization target f _target , f _avg represents the average fitness value of the first subgroup _Q1 ; to the current second chromosome

Two individuals in are crossed over to generate a new second chromosome

Indicates the third chromosome after crossover,

Indicates the fourth chromosome after crossover; the crossover process is based on the second adaptive strategy model $P_{c2}=\left\{\begin{array}{cc}(l_{\max }-l)/(l_{\max }-l_{\mathrm{avg}}),& l\&Greater\; Equal;l_{\mathrm{avg}}\\ 1.0,& l<l_{\mathrm{avg}}\end{array}\right.$ carried out; P _c2 represents the crossover probability of the second subgroup _Q2 , also known as the second crossover probability, l _max represents the best individual fitness value in the second subgroup _Q2 , and l represents the two individuals to be crossover The more adaptive fitness value in , and l=max{l ₁ ,l ₂ }, l ₁ represents the chromosome

The value of the corresponding power optimization target l _target , l ₂ represents the chromosome

The value of the corresponding power optimization target l _target , l _avg represents the average fitness value of the second subgroup _Q2 ; Step 403: Compare f ₁ and f ₃ and f ₂ and f ₄ , if f ₁ ≥ f ₃ and f ₂ ≥ f ₄ , use AQ _cross instead of AQ _current ; if f ₁ < f ₃ or f ₂ < f ₄ , then AQ is _currently unchanged; f ₃ means the first chromosome after crossover

The value of the corresponding VSWR optimization target f _target , f ₄ means the second chromosome after crossover

The value of the corresponding SWR optimization target f _target ; Compare l ₁ and l ₃ and l ₂ and l ₄ , if l ₁ ≤ _{l 3} and l ₂ ≤ l ₄ , use BQ _cross instead of BQ _current ; if l ₁ > l ₃ or l ₂ > l ₄ , then BQ _Currently unchanged; l ₃ means the third chromosome after crossover

The value of the corresponding power optimization target l _target , l ₄ means the fourth chromosome after crossover

The value of the corresponding power optimization target l _target ; Step 404: For the current first chromosome

The two individuals in are mutated separately to generate the mutated first chromosome

express

mutated chromosomes, express

Chromosomes after mutation; the mutation processing is based on the third adaptive strategy model

carried out; P _m1 represents the mutation probability of the first subgroup Q ₁ (also called the first mutation probability), f _min represents the best individual fitness value in the first subgroup Q1, and f _avg represents the first subgroup Q ₁ The average fitness value of , f' is the fitness value of the individual that needs to be mutated, and f ₁ means chromosome

The value of the corresponding SWR optimization target f _target , f ₂ represents the chromosome

The value of the corresponding SWR optimization target f _target ; to the current second chromosome

The two individuals in are mutated separately to generate the mutated second chromosome

express mutated chromosomes,

express

Chromosomes after mutation; the mutation processing is based on the fourth adaptive strategy model $P_{m2}=\left\{\begin{array}{cc}0.5(l_{\max }-l^{\&prime;})/(l_{\max }-l_{\mathrm{avg}}),& l^{\&prime;}\&Greater\; Equal;l_{\mathrm{avg}}\\ 0.5,& l^{\&prime;}<l_{\mathrm{avg}}\end{array}\right.$ carried out; P _m2 represents the mutation probability of the second subgroup _Q2 , also known as the second mutation probability, l _max represents the best individual fitness value in the second subgroup _Q2 , and l _avg represents the second subgroup _Q2 The average fitness value of , l' is the fitness value of the individual to be mutated and

l ₁ means chromosome

The value of the corresponding power optimization target l _target , l ₂ represents the chromosome

The value of the corresponding power optimization target l _target ; Step 405: compare f ₁ and f ₅ , if f ₁ > f ₅ , use

replace

If f ₁ ≤ f ₅ , then

No change; f ₅ means

mutated chromosome

The value of the corresponding SWR optimization target f _target ; Compare f ₂ and f ₆ , if f ₂ > f ₆ , use replace If f ₂ ≤ f ₆ , then

No change; f ₆ means

mutated chromosome

The value of the corresponding SWR optimization target f _target ; Compare l ₁ and l ₅ , if l ₁ < l ₅ , use

replace If l ₁ ≥ _{l 5} , then

unchanged; l ₅ means

mutated chromosome

The value of the corresponding power optimization target l _target ; Compare l ₂ and l ₆ , if l ₂ <l ₆ , use

replace

If l ₂ ≥ _{l 6} , then

unchanged; l ₆ means

mutated chromosome The value of the corresponding power optimization target l _target ; Repeat steps 401 to 405 until all the cross-mutation of chromosomes in the first subgroup _Q1 and the second subgroup _Q2 is completed, and the optimal optimization quantity of the first subgroup _Q1 of the current generation is obtained, that is, the first optimization quantity _DQ1 , the optimal optimization quantity of the second subgroup Q ₂ , that is, the second optimization quantity DQ ₂ ; Step 5: According to the objective function M _objective = {f _objective , l _objective }, traverse all chromosomes in the optimized total population Q _total ' to obtain the current generation optimal individual in the genetic algorithm; In step 5, the first optimized quantity DQ ₁ and the second optimized quantity DQ ₂ are combined to form a new population, that is, the optimized total population Q _total ', and the total optimized population Q is traversed according to the objective function M _target = {f _target , l _target } All the chromosomes in _{the total} ' get the optimal individual of the current generation in the genetic algorithm and put

Assign to I _hbest so that the best individual of the next generation can be compared with the best individual of the current generation, select a better individual from the two, and assign to I _hbest ; Judging whether the termination condition of the genetic algorithm is reached, if the termination condition of the genetic algorithm is not satisfied, then return to step 4, if the termination condition of the genetic algorithm is met, then the optimal individual I _hbest in the genetic algorithm is obtained, and retained, and then step 6 is entered; The termination condition of the genetic algorithm refers to whether the number of iteration steps K is 0, if the number of iteration steps K is not 0, then return to step 3, if the number of iteration steps K is 0, then the optimal individual I _hbest in the genetic algorithm is obtained, And keep it, enter step six; in order to correspond to the expression form of the variable X={XC _a , XL _b , XR _d , XT _e } to be optimized, the expression form of the optimal solution I _hbest set of the electronic component parameters is $I_{\mathrm{hbest}}=\left[\begin{array}{c}{C}_{C1}^{\mathrm{hbest}},C_{C2}^{\mathrm{hbest}},C_{C3}^{\mathrm{hbest}},C_{C4}^{\mathrm{hbest}},C_{C5}^{\mathrm{hbest}}\\ L_{L1}^{\mathrm{hbest}},L_{L2}^{\mathrm{hbest}},L_{L3}^{\mathrm{hbest}},L_{L4}^{\mathrm{hbest}},L_{L5}^{\mathrm{hbest}}\\ R_{R1}^{\mathrm{hbest}},R_{R2}^{\mathrm{hbest}},R_{R3}^{\mathrm{hbest}}\\ T_e^{\mathrm{hbest}}\end{array}\right];$ in,

Indicates the optimal capacitance value of the first capacitance population XC _C1 after genetic algorithm;

Indicates the optimal capacitance value of the second capacitance population XC _C2 after genetic algorithm;

Indicates the optimal capacitance value of the third capacitor population XC _C3 after genetic algorithm;

Indicates the optimal capacitance value of the fourth capacitance population XC _C4 after genetic algorithm;

Indicates the optimal capacitance value of the fifth capacitor population XC _C5 after genetic algorithm;

Indicates the optimal inductance value of the first inductance population XL _L1 after genetic algorithm;

Indicates the optimal inductance value of the second inductance population XL _L2 after genetic algorithm;

Indicates the optimal inductance value of the third inductance population XL _L3 after genetic algorithm;

Indicates the optimal inductance value of the fourth inductance population XL _L4 after genetic algorithm;

Indicates the optimal inductance value of the fifth inductance population XL _L5 after genetic algorithm;

Indicates the optimal electrical group value of the first resistance population XR _R1 after genetic algorithm;

Indicates the optimal electrical group value of the second resistance population XR _R2 after genetic algorithm;

Indicates the optimal electrical group value of the third resistance population XR _R3 after genetic algorithm;

Indicates the transformer population XT. The input/output voltage ratio of the optimal transformer after genetic algorithm; Step 6: Perform initial annealing assignment on the best individual I _hbest of the current generation to obtain the initial individual I _initial ; $I_{\mathrm{hbest}}=\left[\begin{array}{c}{C}_{C1}^{\mathrm{hbest}},C_{C2}^{\mathrm{hbest}},C_{C3}^{\mathrm{hbest}},C_{C4}^{\mathrm{hbest}},C_{C5}^{\mathrm{hbest}}\\ L_{L1}^{\mathrm{hbest}},L_{L2}^{\mathrm{hbest}},L_{L3}^{\mathrm{hbest}},L_{L4}^{\mathrm{hbest}},L_{L5}^{\mathrm{hbest}}\\ R_{R1}^{\mathrm{hbest}},R_{R2}^{\mathrm{hbest}},R_{R3}^{\mathrm{hbest}}\\ T_e^{\mathrm{hbest}}\end{array}\right]$ Perform initial annealing assignment, then the initial individual with simulated annealing algorithm is

in,

Represents the initial value of the first capacitance population XC _C1 set in the simulated annealing algorithm, and

Represents the initial value of the second capacitance population XC _C2 set in the simulated annealing algorithm, and

Represents the initial value of the third capacitance population XC _C3 set in the simulated annealing algorithm, and

Represents the initial value of the fourth capacitance population XC _C4 set in the simulated annealing algorithm, and

Represents the initial value of the fifth capacitance population XC _C5 set in the simulated annealing algorithm, and

Represents the initial value of the first inductance population XL _L1 set in the simulated annealing algorithm, and

Represents the initial value of the second inductance population XL _L2 set in the simulated annealing algorithm, and

Represents the initial value of the third inductance population XL _L3 set in the simulated annealing algorithm, and

Represents the initial value of the fourth inductance population XL _L4 set in the simulated annealing algorithm, and Indicates the initial value of the fifth inductance population XL _L5 set in the simulated annealing algorithm, and

Represents the initial value of the first resistance population XR _R1 set in the simulated annealing algorithm, and

Represents the initial value of the second resistance population XR _R2 set in the simulated annealing algorithm, and Represents the initial value of the third resistance population XR _R3 set in the simulated annealing algorithm, and

Represents the initial value of the transformer population XT _e set in the simulated annealing algorithm, and

Step 7: Assign a trial value to the best individual I _hbest of the current generation to obtain the trial individual I _trial ; The initial individual for the simulated annealing algorithm

If tentative assignment is performed, there will be a new tentative value individual

in, is randomly generated by the first capacitor population XC _C1 within the variable value range of Δx _C1 ,

is randomly generated by the second capacitor population XC _C2 within the variable value range of Δx _C2 ,

is randomly generated by the third capacitor population XC _C3 within the variable value range of Δx _C3 ,

is randomly generated by the fourth capacitor population XC _C4 within the variable value range of Δx _C4 ,

is randomly generated by the fifth capacitor population XC _C5 within the variable value range of Δx _C5 ,

is randomly generated by the first inductance population XL _L1 within the variable value range of Δx _L1 ,

is randomly generated by the second inductance population XL _L2 within the variable value range of Δx _L2 ,

is randomly generated by the third inductance population XL _L3 within the variable value range of Δx _L3 ,

is randomly generated by the fourth inductance population XL _L4 within the variable value range of Δx _L4 ,

is randomly generated by the fifth inductance population XL _L5 within the variable value range of Δx _L5 ,

is randomly generated by the first resistance population XR _R1 within the variable value range of Δx _R1 ,

is randomly generated by the second resistance population XR _R2 within the variable value range of Δx _R2 ,

is randomly generated by the third resistance population XR _R3 within the variable value range of Δx _R3 ,

is randomly generated by the transformer population XT _e within the variable value range Δx _Te ,

Step 8: Perform simulated annealing optimization according to the objective function M _target ={f _target , l _target }, the initial individual I _initial and the trial individual I _trial to obtain the optimized solution of the electronic component parameters; Step 801: Calculate the trial individual

The values of the corresponding objective function M _target = {f _target , l _target } are ff ₁ and ll ₁ respectively, ff ₁ represents the value of the SWR optimization target f _target corresponding to the trial individual I _trial , ll ₁ represents the value of the trial individual I _trial The value of the corresponding power optimization target l _target ; Step 802: Calculate the initial individual

The values of the corresponding objective function value M _target = {f _target , l _target } are ff ₂ and ll ₂ respectively, ff ₂ represents the value of the _initial SWR optimization target f _target corresponding to the initial individual I, and ll ₂ represents the value of the initial individual I The value of the _initial corresponding power optimization target l _target ; Step 803: Judging whether the values ff ₁ and ll ₂ of the objective function value Mtarget={ _ftarget , _ltarget } of the _trial individual I are better than _{the initial} corresponding objective function value _Mtarget ={ _ftarget , _ltarget} of the initial individual I } values ff ₂ and ll ₂ , if ff ₁ ≤ ff ₂ and ll ₁ ≥ ll ₂ , enter step 804, otherwise return to step 801; Step 804: Calculate whether the trial individual I _trial satisfies the receiver function relationship $\left[\begin{array}{c}{P}_{\mathrm{VSWR}}=\exp \left(\frac{{\mathrm{ff}}_1-{\mathrm{ff}}_2}{T_{\mathrm{now}}}\right)>r\&Element;\left[\mathrm{0,1}\right]\\ P_G=\exp \left(\frac{{\mathrm{ll}}_2-{\mathrm{ll}}_1}{T_{\mathrm{now}}}\right)>r\&Element;\left[\mathrm{0,1}\right]\end{array}\right.,$ If it is satisfied, the trial individual I _will replace the _initial individual I, and enter step 805; if not, return to step 801; P _VSWR represents the receiving probability corresponding to the standing wave ratio, _PG represents the receiving probability corresponding to the conversion gain, and T _now represents The current temperature, r is a random number generated randomly with the probability of a uniform distribution function in the range [0,1], that is, r=RAN(0,1) Step 805: In the annealing algorithm, the temperature is lowered at the cooling speed a, and it is judged whether the current temperature T _now is less than the cut-off temperature T _end , if T _now is greater than T _end , return to step 801; if T _now is less than or equal to T _end , then set The optimized initial individual I _{is initially used} as the electronic component parameter optimization solution I _best after genetic-simulated annealing treatment, ${\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; the\; best</span>}}<span\; class="notranslate">\; =</span>\left[\begin{array}{c}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; the\; best</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; the\; best</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; 3</span>}}^{\mathrm{<span\; class="notranslate">\; the\; best</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; 4</span>}}^{\mathrm{<span\; class="notranslate">\; the\; best</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; 5</span>}}^{\mathrm{<span\; class="notranslate">\; the\; best</span>}}\\ {\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; the\; best</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; the\; best</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; 3</span>}}^{\mathrm{<span\; class="notranslate">\; the\; best</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; 4</span>}}^{\mathrm{<span\; class="notranslate">\; the\; best</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; 5</span>}}^{\mathrm{<span\; class="notranslate">\; the\; best</span>}}\\ {\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; R</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; the\; best</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; R</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; the\; best</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; R</span>}\mathrm{<span\; class="notranslate">\; 3</span>}}^{\mathrm{<span\; class="notranslate">\; the\; best</span>}}\\ {\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; the\; best</span>}}\end{array}\right]<span\; class="notranslate">\; .</span>$ 2. adopt genetic-simulated annealing combination according to claim 1 to the optimization method of electronic component parameter in antenna broadband matching network, it is characterized in that: if first subpopulation Q ₁ has adopted objective function M _target ={f _target , The antenna standing wave ratio f _target in the l _target } is optimized, then the second subgroup _Q2 should adopt the conversion power gain l target in the objective function M _target ={f _target , l _target } to optimize; otherwise _, if the second The subgroup Q ₂ adopts the antenna standing wave ratio f _objective in the objective function M _objective = {f _objective , l _objective } to optimize, then the first subgroup Q ₁ should use the objective function M _objective = {f _objective , l _objective } The conversion power gain l _target in is optimized. 3. adopt genetic-simulated annealing combination according to claim 1 to the optimization method of electronic component parameter in antenna broadband matching network, it is characterized in that: the input/output voltage ratio of the transformer T0 that adopts is DT:1. 4. The method for optimizing the parameters of the electronic components in the antenna broadband matching network by using genetic-simulated annealing combination according to claim 1, characterized in that: the values of the variables m, w, v and n of the chromosome are 200. 5. The method for optimizing the electronic component parameters in the antenna broadband matching network by using genetic-simulated annealing combination according to claim 1, characterized in that: the number of iteration steps K selected in the genetic algorithm is 250. 6. according to claim 1, adopt genetic-simulated annealing combination to optimize the electronic component parameter in antenna broadband matching network, it is characterized in that: the initial temperature T selected in the described simulated annealing algorithm is 0.05 degree, cut-off temperature T _end is 0.00001 degrees, and the cooling rate a is 0.9.

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