融入重心反向学习和单纯形搜索的粒子群优化算法  

A particle swarm optimization algorithm with centroid opposition-based learning and simplex search

作　　者：张文宁[1,2] 周清雷[3] 焦重阳 梅亮 ZHANG Wen-ning;ZHOU Qing-lei;JIAO Chong-yang;MEI Liang(State Key Laboratory of Mathematical Engineering and Advanced Computing,University of Information Engineering,Zhengzhou 450001;Software College,Zhongyuan University of Technology,Zhengzhou 450007;School of Information Engineering,Zhengzhou University,Zhengzhou 450001,China)

机构地区：[1]信息工程大学数学工程与先进计算国家重点实验室,河南郑州450001 [2]中原工学院软件学院,河南郑州450007 [3]郑州大学信息工程学院,河南郑州450001

出　　处：《计算机工程与科学》2023年第9期1629-1638,共10页Computer Engineering & Science

基　　金：河南省科技攻关计划(172102210592,212102210417)。

摘　　要：针对粒子群优化PSO算法后期种群多样性差和易陷入局部最优解等问题,提出具备重心反向学习和单纯形搜索行为的粒子群优化COLS-PSO算法。初始时,基于混沌策略构造出搜索空间。进化过程中,基于Spearman系数选择需要进行重心反向学习的粒子,以帮助算法逃离局部极值区域。进一步引入局部搜索能力较强的单纯形搜索方法增强对最优粒子邻近区域的开发,以提高搜索精度。实验先在若干标准测试函数上进行,之后将COLS-PSO算法应用于软件测试数据生成问题。实验结果表明,COLS-PSO算法在求解精度、收敛速度和有效性方面表现较好,能够有效平衡种群多样性和算法收敛性的矛盾。The particle swarm optimization(PSO)algorithm often suffers from problems such as low population diversity and being trapped in local optimal solutions.To address these issues,a particle swarm optimization algorithm with centroid opposition based learning and simplex search(COLS-PSO)is proposed.During the initialization process,the search space is constructed based on a chaos strategy.During the evolution process,the particles that need to undergo centroid opposition-based learning are selected based on the Spearman coefficient to help the algorithm escape from local extreme value areas.Furthermore,a simplex search method with strong local search ability is introduced to enhance the development of the optimal particle's neighboring area and improve the search accuracy.The algorithm is tested on several standard test functions and then applied to software testing data generation problems.The experimental results show that the COLS-PSO algorithm performs well in terms of solution accuracy,convergence speed,and effectiveness,and can effectively balance the contradiction between population diversity and algorithm convergence.

关 键 词：粒子群优化算法 混沌策略 重心反向学习 单纯形搜索 测试数据生成 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]