计算机工程与应用2024，Vol.60Issue(20)：96-115,20.DOI:10.3778/j.issn.1002-8331.2312-0390
种群熵竞争粒子群算法
Population Entropy Competitive Particle Swarm Optimization Algorithm
摘要
Abstract
To further improve the convergence and solution accuracy of competitive swarm optimizer,a variety of popula-tion entropy competitive particle swarm optimization algorithm(CSOPE)is proposed.Firstly,a nonlinear inertia weight adjustment strategy is proposed to balance the global exploration ability and local exploitation ability of particles.Secondly,a population state detection strategy based on entropy model is proposed,which calculates the population entropy by the standardized quartile difference and standardized median difference of the population.The population state is monitored by the difference in entropy values between adjacent generations of the population.When the population is in a conver-gence state,it uses gray wolf search to exploit winner particle locally to improve the convergence accuracy of the algo-rithm.The proposed CSOPE algorithm is compared with other 8 optimization algorithms on 21 test functions in CEC2008 and CEC2013,and the experimental results show that the solving accuracy and convergence of the CSOPE algorithm are significantly improved.The CSOPE algorithm is applied to the node localization problem in wireless sensor networks,and the results show that the CSOPE algorithm has high localization accuracy.关键词
竞争粒子群算法/种群状态/种群熵/惯性权重/灰狼搜索Key words
competitive particle swarm optimization algorithm/population status/population entropy/inertial weight/grey wolf search分类
信息技术与安全科学引用本文复制引用
王霞,王卓然,张珊,王勇..种群熵竞争粒子群算法[J].计算机工程与应用,2024,60(20):96-115,20.基金项目
云南省科技厅基础研究专项(202201AT070021). （202201AT070021）
