一种自适应多种群的PSO算法

doi:10.16182/j.issn1004731x.joss.201612002

系统仿真学报 ›› 2016, Vol. 28 ›› Issue (12): 2887-2896.doi: 10.16182/j.issn1004731x.joss.201612002

一种自适应多种群的PSO算法

夏学文¹, 王博建¹, 金畅¹, 何国良², 谢承旺¹, 魏波¹

1.华东交通大学软件学院,智能优化与信息处理研究所,江西南昌 330013;
2.武汉大学计算机学院,湖北武汉 430079

收稿日期:2015-12-21 修回日期:2016-03-01 出版日期:2016-12-08 发布日期:2020-08-13
作者简介:夏学文(1974-）,男,湖北云梦,博士,副教授,研究方向为计算智能及其应用。
基金资助:
国家自然科学基金(61663009, 61602174, 61562028), 江西省自然科学基金(20161BAB202064, 20151BAB207022, 20161BAB212052), 江西省教育厅科研项目(GJJ150539, GJJ150496)

Self-adaptive Multi-swarm Particle Swarm Optimization Algorithm

Xia Xuewen¹, Wang Bojian¹, Jin Chang¹, He Guoliang², Xie Chengwang¹, Wei Bo¹

1. Intelligence optimization & Information Process Lab., East China Jiaotong University, School of Software, Nanchang 330013, China;
2. School of Computer, Wuhan University, Wuhan 430079, China

Received:2015-12-21 Revised:2016-03-01 Online:2016-12-08 Published:2020-08-13

摘要/Abstract

摘要： 针对粒子群算法易早熟收敛、逃离局部最优能力差、精度低等缺点,提出了一种自适应多种群PSO算法(Self-adaptive Multi-swarm Particle Swarm Optimization,SMPSO)。算法通过多个子种群独立进化和自适应重组操作既保持了种群多样性又实现了子种群间的信息共享与交互;同时,通过对粒子历史最优解进行周期性采样与统计,进而指导算法进行探测操作,不仅增强算法的全局搜索能力,也提高其跳出局部最优的能力;最后,引入了两种局部搜索策略提升了算法的收敛速度和求解精度。通过和其它PSO算法在标准测试函数和工程应用的实验对比表明,SMPSO在逃逸能力、收敛速度和求解精度上有显著提高。

关键词: 粒子群算法, 多种群, 自适应, 探测操作, 局部搜索

Abstract: To overcome the shortcomings of poor ability to escape a local optimal, premature convergence and low precision of the traditional particle swarm optimization algorithm (PSO), a self-adaptive multi-swarm particle swarm optimization (SMPSO) was proposed. In SMPSO, the whole population was divided into many parallel-evolution multi-swarms, the aim of which was to keep diversity of the population. Furthermore, a self-adaptive regrouping operator was proposed to reinforce the information sharing and interaction between different swarms. In addition, particles’ historical information were periodic sampling and the statistics results were used to direct the best solution to carry out a detecting operator. The aim of the strategy was to improve PSO’s global searching ability and to help the population escape a local optimal solution. To accelerate convergence speed and improve solutions’ accuracy of PSO, two local search strategies were proposed. The comparisons of SMPSO with other five PSO algorithms on some benchmark functions and an engineering application indicate that the proposed strategies can effectively enhance the ability of escaping local optimal solution, and speed up the convergence and raised solutions’ accuracy.

Key words: particle swarm optimization, multi-swarm, self-adaptive, detecting operator, local search

中图分类号:

TP301

夏学文, 王博建, 金畅, 何国良, 谢承旺, 魏波. 一种自适应多种群的PSO算法[J]. 系统仿真学报, 2016, 28(12): 2887-2896.

Xia Xuewen, Wang Bojian, Jin Chang, He Guoliang, Xie Chengwang, Wei Bo. Self-adaptive Multi-swarm Particle Swarm Optimization Algorithm[J]. Journal of System Simulation, 2016, 28(12): 2887-2896.

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一种自适应多种群的PSO算法

Self-adaptive Multi-swarm Particle Swarm Optimization Algorithm

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