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Open AccessArticle
Improved Osprey Optimization Algorithm Based on Two-Color Complementary Mechanism for Global Optimization and Engineering Problems
by
Fengtao Wei
Fengtao Wei *,
Xin Shi
Xin Shi and
Yue Feng
Yue Feng
School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
*
Author to whom correspondence should be addressed.
Biomimetics 2024, 9(8), 486; https://doi.org/10.3390/biomimetics9080486 (registering DOI)
Submission received: 20 June 2024
/
Revised: 9 August 2024
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Accepted: 10 August 2024
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Published: 12 August 2024
Abstract
Aiming at the problem that the Osprey Optimization Algorithm (OOA) does not have high optimization accuracy and is prone to falling into local optimum, an Improved Osprey Optimization Algorithm Based on a Two-Color Complementary Mechanism for Global Optimization (IOOA) is proposed. The core of the IOOA algorithm lies in its unique two-color complementary mechanism, which significantly improves the algorithm’s global search capability and optimization performance. Firstly, in the initialization stage, the population is created by combining logistic chaos mapping and the good point set method, and the population is divided into four different color groups by drawing on the four-color theory to enhance the population diversity. Secondly, a two-color complementary mechanism is introduced, where the blue population maintains the OOA core exploration strategy to ensure the stability and efficiency of the algorithm; the red population incorporates the Harris Hawk heuristic strategy in the development phase to strengthen the ability of local minima avoidance; the green group adopts the strolling and wandering strategy in the searching phase to add stochasticity and maintain the diversity; and the orange population implements the optimized spiral search and firefly perturbation strategies to deepen the exploration and effectively perturb the local optimums, respectively, to improve the overall population diversity, effectively perturbing the local optimum to improve the performance of the algorithm and the exploration ability of the solution space as a whole. Finally, to validate the performance of IOOA, classical benchmark functions and CEC2020 and CEC2022 test sets are selected for simulation, and ANOVA is used, as well as Wilcoxon and Friedman tests. The results show that IOOA significantly improves convergence accuracy and speed and demonstrates high practical value and advantages in engineering optimization applications.
Share and Cite
MDPI and ACS Style
Wei, F.; Shi, X.; Feng, Y.
Improved Osprey Optimization Algorithm Based on Two-Color Complementary Mechanism for Global Optimization and Engineering Problems. Biomimetics 2024, 9, 486.
https://doi.org/10.3390/biomimetics9080486
AMA Style
Wei F, Shi X, Feng Y.
Improved Osprey Optimization Algorithm Based on Two-Color Complementary Mechanism for Global Optimization and Engineering Problems. Biomimetics. 2024; 9(8):486.
https://doi.org/10.3390/biomimetics9080486
Chicago/Turabian Style
Wei, Fengtao, Xin Shi, and Yue Feng.
2024. "Improved Osprey Optimization Algorithm Based on Two-Color Complementary Mechanism for Global Optimization and Engineering Problems" Biomimetics 9, no. 8: 486.
https://doi.org/10.3390/biomimetics9080486
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