基于混沌局部搜索的粒子群算法及其应用  

Particle Swarm Algorithm Based on Chaos Local Search and Its Application

作　　者：刘道文[1] 杨拥军 LIU Dao-wen;YANG Yong-jun(School of Electrical&Mechano-Electronic Engineering,Xuchang University,Xuchang 461000,China)

机构地区：[1]许昌学院电气机电工程学院,河南许昌461000

出　　处：《计算机技术与发展》2021年第4期216-220,共5页Computer Technology and Development

基　　金：河南省高等学校重点科研项目(16A520070)。

摘　　要：为提高混沌优化搜索结果的精度,在以粒子群算法进行全局搜索的基础上,根据全局搜索结果利用混沌优化进行局部搜索,实现在全局范围上搜索最优值。分析局部混沌搜索方法,设计基于混沌局部搜索的粒子群算法的流程,利用混沌优化进行粒子群局部搜索以跳出局部最优搜索区域,避免陷入局部极小值和实现在全局范围上搜索目标函数的最优值。以RMSE误差作为搜索结果精度评价指标,通过Rosenbrock函数算例对基于混沌局部搜索的粒子群算法精度进行分析,并将该算法应用于停车场最优选址实际问题的决策。研究结果表明,该算法搜索结果相较于混沌优化算法搜索结果具有更高的精度,其数值更逼近理论最优值,验证了其提高搜索结果精度的有效性和在解决实际问题上的可行性。In order to improve the accuracy of chaos optimization results,based on the global search of particle swarm algorithm,the local search is carried out by using chaos optimization according to the global search results,and the optimal value is searched in the global range.Then we analyze the local chaos search method and design the process of particle swarm algorithm based on the chaos local search.Chaos optimization is used in particle swarm algorithm local search to jump out of the local search area,so as to avoid falling into the local minimum and achieve the optimal value of the objective function in the global range.Taking RMSE error as the accuracy evaluation criteria of search results,the accuracy of particle swarm algorithm based on chaos local search is analyzed by Rosenbrock function example,and subsequently the algorithm is applied to the decision-making of optimal location of parking lot.Proved by the research,the search results of the proposed algorithm have higher accuracy than those of chaos optimization algorithm,and its numerical value is closer to the theoretical optimal value,which verifies the effectiveness of improving the accuracy of the search results and the feasibility of solving practical problems.

关 键 词：混沌优化 局部搜索 全局搜索 粒子群算法 最优选址 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]