An Improved Bat Algorithm For Solving Nonlinear Algebraic Systems Of Equations
Pages 75 - 81
Abstract
This paper introduces a new hybrid bat algorithm for solving nonlinear equations. Nonlinear equations are often solved by using various optimization algorithms in practice. Bat algorithm is a new intelligent optimization algorithm proposed by Yang in 2010. However, in its practical application, there are some disadvantages, for example, it is easy to fall into local optimization. Therefore, this paper introduces four methods: adaptive inertia weight, chaotic local search, differential evolution algorithm and cross entropy method to optimize the bat algorithm. In this paper, the implementation of four algorithms is first introduced, and then four examples of nonlinear system of equations are to be calculated by using the hybrid self-adaptive bat algorithm (HSABA) to prove the practical feasibility of these four methods.
References
[1]
Yasmine Aboubi, Habiba Drias, and Nadjet Kamel. 2016. BAT-CLARA: BAT-inspired algorithm for Clustering LARge Applications. IFAC-PapersOnLine 49, 12 (2016), 243–248.
[2]
Tripathi Ashish, Sharma Kapil, and Bala Manju. 2018. Parallel bat algorithm-based clustering using mapreduce. In Networking Communication and Data Knowledge Engineering. Springer, 73–82.
[3]
Leandro dos Santos Coelho and Alireza Askarzadeh. 2016. An enhanced bat algorithm approach for reducing electrical power consumption of air conditioning systems based on differential operator. Applied Thermal Engineering 99 (2016), 834–840.
[4]
Iztok Fister, Simon Fong, and Janez Brest. 2014. A novel hybrid self-adaptive bat algorithm. The Scientific World Journal 2014 (2014).
[5]
Iztok Fister Jr, Dušan Fister, and Xin-She Yang. 2013. A hybrid bat algorithm. arXiv preprint arXiv:1303.6310(2013).
[6]
Sonia Goyal and Manjeet Singh Patterh. 2016. Modified bat algorithm for localization of wireless sensor network. Wireless Personal Communications 86, 2 (2016), 657–670.
[7]
Eslam A Hassan, Ahmed Ibrahem Hafez, Aboul Ella Hassanien, and Aly A Fahmy. 2015. A discrete bat algorithm for the community detection problem. In International conference on hybrid artificial intelligence systems. Springer, 188–199.
[8]
Arvinder Kaur and Yugal Kumar. 2021. Recent Developments in Bat Algorithm: A Mini Review. In Journal of Physics: Conference Series, Vol. 1950. IOP Publishing, 012055.
[9]
CHAOUCHE RAMDANE Lamia and MAHI Habib. 2020. K-means using Bat algorithm for remotely sensed data clustering. Algerian Journal of Geosciences and Remote Sensing (2020).
[10]
Seyedali Mirjalili, Seyed Mohammad Mirjalili, and Xin-She Yang. 2014. Binary bat algorithm. Neural Computing and Applications 25, 3 (2014), 663–681.
[11]
Muhammad Zubair Rehman, Kamal Z Zamli, and Abdullah Nasser. 2020. An Improved Genetic Bat algorithm for Unconstrained Global Optimization Problems. In Proceedings of the 2020 9th International Conference on Software and Computer Applications. 94–98.
[12]
Reuven Rubinstein. 1999. The cross-entropy method for combinatorial and continuous optimization. Methodology and computing in applied probability 1, 2(1999), 127–190.
[13]
Yuhui Shi and Russell Eberhart. 1998. A modified particle swarm optimizer. In 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No. 98TH8360). IEEE, 69–73.
[14]
Xin-She Yang. 2010. A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, 65–74.
[15]
Xuanping Zhang, Yuping Du, Guoqiang Qin, and Zheng Qin. 2005. An improved particle swarm optimization algorithm for adaptive inertial weights. Journal of Xi’an Jiaotong University 39, 10 (2005), 1039–1042.
[16]
Xinming Zhang, Qian Wan, and Youhua Fan. 2019. Applying modified cuckoo search algorithm for solving systems of nonlinear equations. Neural Computing and Applications 31, 2 (2019), 553–576.
Index Terms
- An Improved Bat Algorithm For Solving Nonlinear Algebraic Systems Of Equations
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Published In
May 2022
143 pages
ISBN:9781450396097
DOI:10.1145/3545801
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Published: 09 September 2022
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ICBDC 2022
ICBDC 2022: 2022 7th International Conference on Big Data and Computing
May 27 - 29, 2022
Shenzhen, China
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