Hybrid chimp optimization algorithm with artificial preference weight and its application

被引：0

作者：

Liu W. ^{[1
,2
]}

Niu Y.-J. ^{[1
,2
]}

Wang D. ^{[3
]}

Liu G.-W. ^{[3
]}

Ma L.-X. ^{[1
,2
]}

机构：

[1] College of Science, Liaoning Technical University, Fuxin

[2] Institute of Intelligent Engineer and Mathematics, Liaoning Technical University, Fuxin

[3] College of Mines, Liaoning Technical University, Fuxin

来源：

Kongzhi yu Juece/Control and Decision | 2024年 / 39卷 / 02期

关键词：

arithmetic optimization algorithm; artificial preference weight; chimp optimization algorithm; dissimilarity; engineering design optimization; nonlinear convergence factor;

D O I：

10.13195/j.kzyjc.2022.0760

中图分类号：

学科分类号：

摘要：

A hybrid chimp optimization algorithm (HChOA) with artificial preference weight is proposed to improve the convergence speed, solution accuracy and local extreme escape ability of the chimp optimization algorithm. Firstly, combined with the actual situation of the ChOA, a new nonlinear convergence factor is designed for balancing global and local search capability. Secondly, the concept of “dissimilarity”and an artificial preference weight which can be described as “tendency difference and repulsion similarity”are introduced into the chimp population to optimize position update rule, enhance the population diversity at the end of iteration and accelerate the convergence speed of the algorithm. Finally, an improved arithmetic optimization algorithm (IAOA) and mixing into ChOA are proposed, we extract some chimp individuals to perform the IAOA optimization strategy to avoid group search stagnation and premature convergence caused by leader falling into local optimum. Through the numerical comparison experiments of 8 benchmark functions in various dimensions and the solution of one engineering design problem, the comprehensive analysis verifies that the HChOA has significant superiority, robusticity and the value of engineering application. © 2024 Northeast University. All rights reserved.

引用

页码：411 / 419

页数：8

共 20 条

[1]

Khishe M, Mosavi M R., Chimp optimization algorithm, Expert Systems with Applications, 149, (2020)

[2]

Jia H M, Sun K J, Zhang W Y, Et al., An enhanced chimp optimization algorithm for continuous optimization domains, Complex & Intelligent Systems, 8, 1, pp. 65-82, (2022)

[3]

He Q, Luo S H., Chimp optimization algorithm based on hybrid improvement strategy and its mechanical application, Contral and Decision, 38, 2, pp. 354-364, (2023)

[4]

Kaur M, Kaur R, Singh N, Et al., SChoA: A newly fusion of sine and cosine with chimp optimization algorithm for HLS of datapaths in digital filters and engineering applications, Engineering with Computers, 38, 2, pp. 975-1003, (2022)

[5]

Liu C H, He Q., Golden sine chimp optimization algorithm integrating multiple strategies, Acta Automatica Sinica

[6]

Zeng J C, Jie J, Cui Z H., Particle swarm optimization algorithm, (2004)

[7]

Zhang M J, Long D Y, Wang X, Et al., Research on convergence of grey wolf optimization algorithm based on Markov chain, Acta Electronica Sinica, 48, 8, pp. 1587-1595, (2020)

[8]

Abualigah L, Diabat A, Mirjalili S, Et al., The arithmetic optimization algorithm, Computer Methods in Applied Mechanics and Engineering, 376, (2021)

[9]

Wu T B, Gui W H, Yang C H, Et al., Improved grey wolf optimization algorithm with logarithm function describing convergence factor and its application, Journal of Central South University: Science and Technology, 49, 4, pp. 857-864, (2018)

[10]

Wei Z L, Zhao H, Li M D, Et al., A grey wolf optimization algorithm based on nonlinear adjustment strategy of control parameter, Journal of Air Force Engineering University: Natural Science Edition, 17, 3, pp. 68-72, (2016)

← 1 2 →