融合多策略改进的白鲸优化算法  

Beluga Whale Optimization with Improved Multi-Strategy Integration Problem

作　　者：柴岩[1] 常晓萌 任生 CHAI Yan;CHANG Xiaomeng;REN Sheng(College of Science,Liaoning Technical University,Fuxin,Liaoning 123000,China)

机构地区：[1]辽宁工程技术大学理学院,辽宁阜新123000

出　　处：《计算机工程与应用》2025年第5期76-93,共18页Computer Engineering and Applications

基　　金：辽宁省科研项目(LJKMZ20220694);辽宁省教育厅项目(LJ2019JL017)。

摘　　要：为进一步提升白鲸优化算法(BWO)的寻优能力和收敛速度,提出一种融合多策略改进的白鲸优化算法(multi-strategy improved beluga whale optimization,MIBWO)。针对算法初期因随机生成个体的遍历性较差使得算法易陷入局部的劣势,利用PWLCM混沌映射增加种群多样性以及准反向学习生成的反向解增强初始解的质量,为算法寻优性能奠定基础;构造一种动态限制局部扰动搜索机制,引入非线性收敛因子扰动个体增加求解精度与速度,为避免收敛因子在迭代后期过快收敛,利用动态平衡搜索策略以避免陷入局部最优;提出一种差异性种群进化策略对鲸鱼坠落阶段进行最优值位置扰动更新,有效提升收敛精度。理论分析和数值实验证明MIBWO算法具有较强的寻优性能,MIBWO算法在PV辨识问题体现了良好的寻优性能、收敛速度及鲁棒性并具有一定的实际工程应用前景。In order to further improve the optimization ability and convergence speed of the beluga whale optimization(BWO),a multi-strategy improved beluga whale optimization(MIBWO)algorithm based on multi-strategy improvement is proposed.In order to avoid the rapid convergence of the convergence factor in the late iteration,the dynamic equilibrium search strategy is used to increase the population diversity and the reverse solution generated by quasi-reverse learning to enhance the quality of the initial solution,which lays the foundation for the optimization performance of the algorithm.Theoretical analysis and numerical experiments show that the MIBWO algorithm has strong optimization performance.The MIBWO algorithm has good optimization performance,convergence speed and robustness in PV identification,and has certain practical engineering application prospects.

关 键 词：白鲸优化算法 PWLCM混沌映射 准反向学习 非线性收敛因子 动态平衡搜索策略 差异性种群进化策略 PV辨识问题 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]