融合多策略改进的鲸鱼优化算法  

Whale Optimization Algorithm with Improved Multi-Strategy Integration

作　　者：王玉芳 程培浩 WANG Yufang;CHENG Peihao(School of Statistics,Tianjin University of Finance and Economics,Tianjin 300221,China)

机构地区：[1]天津财经大学统计学院,天津300221

出　　处：《计算机工程与应用》2025年第8期83-99,共17页Computer Engineering and Applications

基　　金：国家社会科学基金(19CGL002);天津财经大学优秀青年教师支持计划。

摘　　要：为解决鲸鱼优化算法(whale optimization algorithm,WOA)的收敛精度低和易陷入局部最优等缺点,提出一种多策略改进的鲸鱼优化算法(multi-strategy improved whale optimization algorithm,MSWOA)。设计一种动态自适应探索转换概率替代原算法中的随机探索概率,使得靠近最优个体的优秀个体更易引导全局搜索,有利于增强解的质量,防止算法陷入局部最优;引入鲸鱼个体聚集度的概念,当鲸鱼陷入聚集状态时采用大步长更新位置,防止迭代后期种群多样性减少;设计一种邻域解变异增强策略同时考虑当前个体与其相邻个体对下一代个体位置的影响,以防止种群进入聚集状态,提高算法跳出局部最优的能力。仿真实验基于CEC2017中29个测试函数和CEC2019中的10个测试函数进行,分别探究了3个改进策略对算法的探索与开发的影响、对种群多样性的影响以及对算法收敛性的影响。收敛性分析、Wilcoxon秩和检验和Fridman检验表明MSWOA具有良好的寻优性和鲁棒性。进一步,将MSWOA应用于压力容器设计和减速器设计问题上,验证了MSWOA在求解实际问题中的有效性和可靠性。In order to solve the shortcomings of the whale optimization algorithm(WOA),such as low convergence accuracy and the tendency to easily fall into local optimum,a multi-strategy improved whale optimization algorithm(MSWOA)is proposed.Firstly,a dynamic adaptive exploration conversion probability is designed to replace the random exploration probability in the original algorithm,so that the excellent individuals close to the optimal individual can easily guide the global search,which is conducive to enhancing the quality of the solution and preventing the algorithm from falling into the local optimum.Secondly,the concept of individual whale aggregation is introduced,and when whales fall into a state of aggregation,large steps are used to update the position to prevent the population diversity from decreasing in the later iteration stage.Finally,a neighborhood demutation enhancement strategy is designed to consider the influence of the current individual and its neighbors on the position of the next generation of individuals,so as to prevent the population from entering the aggregation state and improve the ability of the algorithm to jump out of the local optimum.Based on 29 test functions in the CEC2017 and 10 test functions in the CEC2019,the simulation experiments explore the influence of the three improved strategies on the exploration and development of the algorithm,the impact on population diversity and the convergence of the algorithm.Furthermore,MSWOA is applied to the design of pressure vessel and reducer,and the effectiveness and reliability of MSWOA in solving practical problems are verified.

关 键 词：鲸鱼优化算法 动态自适应探索转换策略 鲸鱼个体聚集度跟随策略 邻域解变异增强策略 工程优化 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]