首页|基于Fuch映射的改进白鲸优化算法及应用

基于Fuch映射的改进白鲸优化算法及应用

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  • 国家科技期刊平台
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针对标准白鲸优化算法(BWO)存在收敛精度低、自适应能力有限和抗停滞能力弱等缺点,从混沌初始化、参数混沌和非线性控制策略3个角度,提出2种基于Fuch映射和动态反向学习的改进白鲸优化算法(CIOEBWO和CPOEBWO).采用Fuch混沌初始化,提高算法初始化种群的遍历性,从而提升算法寻优精度和收敛速度;在开发阶段,引入Fuch混沌映射对参数C1进行动态调节,协调算法的全局搜索和局部搜索,有效提高算法自适应能力;基于上述2种改进方式,分别引入动态反向学习策略,丰富优质个体数量,提升算法整体抗停滞能力.根据8种基本测试函数仿真实验和Friedman秩检验结果可得,改进算法的收敛精度、自适应能力和抗停滞能力均得到了有效提升.与BWO和CIOEBWO相比,CPOEB-WO显现出较为优异的性能.此外,从CPOEBWO与常见的6种对比算法的寻优结果可知,CPOEBWO算法有较强的寻优能力和鲁棒性.最后,为展示CPOEBWO算法的适用性和有效性,将其应用于工程优化问题.
Improved beluga whale optimization algorithms based on Fuch mapping and applications
Aiming at the drawbacks of beluga whale optimization(BWO),such as low convergence accuracy,limited adaptive ability and weak anti-stagnation ability,two improved BWO algorithms based on Fuch mapping and dynamic opposition-based learning,namely,CIOEBWO and CPOEBWO,are pro-posed from the perspectives of chaos initialization,chaotic parameter,and nonlinear control strategy.Fuch chaotic initialization is used to increase the traversal of the initial population of BWO,which en-hances the optimization accuracy and convergence speed of the algorithm.In the phase of exploitation,Fuch chaotic mapping is introduced to dynamically adjust the parameter C1 to coordinate the capabilities of global search and local search,which improves the adaptive ability of BWO effectively.On the basis of two improvement strategies described above,the dynamic opposition-based learning strategy is intro-duced to enrich the number of high-quality individuals and enhance the overall anti-stagnation ability of the algorithm.The experimental results of 8 benchmark test functions and Friedman rank test indicate that the convergence accuracy,adaptive ability,and anti-stagnation ability of improved BWO are effec-tively improved.Compared with BWO and CIOEBWO,CPOEBWO has the better performance.In ad-dition,the optimization results of CPOEBWO and six comparison algorithms show that CPOEBWO has the stronger optimization ability and robustness.Finally,CPOEBWO is applied to solve the engineering optimization problems to demonstrate its applicability and effectiveness.

beluga whale optimization algorithmFuch mappingdynamic opposition-based learningchaotic parameterengineering optimization problems

陈心怡、张孟健、王德光

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贵州大学电气工程学院,贵州 贵阳 550025

华南理工大学计算机科学与工程学院,广东 广州 510006

白鲸优化算法 Fuch映射 动态反向学习 参数混沌策略 工程优化问题

贵州省省级科技计划贵州省教育厅创新群体贵州省教育厅青年科技人才成长项目贵州大学科研基金资助项目

黔科合基础-ZK[2022]一般103黔科合支撑[2021]012黔教合KY字[2022]138号贵大特岗合字[2021]04号

2024

10.3969/j.issn.1007-130X.2024.08.017

计算机工程与科学
国防科学技术大学计算机学院

计算机工程与科学

CSTPCD北大核心
影响因子：0.787
ISSN：1007-130X
年,卷(期)：2024.46(8)
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