基于Logistic映射的新型混沌简化PSO算法  

New Chaotic Simplified Particle Swarm Optimization Algorithm Based on Logistic Mapping

作　　者：杨万里 周雪婷 陈孟娜 YANG Wan-li;ZHOU Xue-ting;CHEN Meng-na(School of Electrical and Information Engineering,Hunan Institute of Technology,Hengyang 421002,China;School of Electrical and Information Engineering,Hunan University,Changsha 410006,China)

机构地区：[1]湖南工学院电气与信息工程学院,湖南衡阳421002 [2]湖南大学电气与信息工程学院,湖南长沙410006

出　　处：《计算机与现代化》2019年第12期15-20,26,共7页Computer and Modernization

基　　金：湖南省教育厅科研项目(17C0431);湖南工学院科研项目(HY15020,2017HY023)

摘　　要：针对基本粒子群算法易陷入局部最优、收敛速度慢、收敛精度差等问题,提出一种基于Logistic映射的新型混沌简化PSO算法(CIW-SPSO)。该算法引入混沌理论使惯性权重具有混沌搜索能力,同时使学习因子随寻优过程呈正弦函数变化,降低算法陷入局部最优的概率。使用6个经典测试函数进行仿真测试,结果表明:本算法收敛速度快,收敛精度高,能避免陷入局部最优,提升算法优化性能。An new chaotic simplified particle swarm optimization algorithm based on logistic mapping(CIW-SPSO)is proposed to tackle the problems of basic PSO algorithm,such as easy to fall into local optimum,slow convergence,and low accuracy.The algorithm introduces chaos theory make inertia weight with chaotic search ability,and make learning factor changing with sine function optimization process,reduce the probability of algorithm falling into local optimum.Six classical test functions are used for simulation.The results show that the CIW-SPSO algorithm has faster convergence speed and higher accuracy,and can avoid local optimum and improve the algorithm optimization performance effectively.

关 键 词：混沌映射 惯性权重 学习因子 简化粒子群算法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]