随机再分配的多群粒子群优化

2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) Pub Date : 2017-09-01 DOI:10.1109/KCIC.2017.8228445

Naufal Suryanto, C. Ikuta, D. Pramadihanto

{"title":"随机再分配的多群粒子群优化","authors":"Naufal Suryanto, C. Ikuta, D. Pramadihanto","doi":"10.1109/KCIC.2017.8228445","DOIUrl":null,"url":null,"abstract":"Particle Swarm Optimization (PSO) is fast and popular algorithm to find the optimum value of non-linear and multi-dimensional function. However, it often easily trapped into local optima because the particles move closer to the best particle quickly. This paper purposes a new algorithm called Multi-Group Particle Swarm Optimization with Random Redistribution (MGRR-PSO) that tried to solve the weakness of standard PSO. MGRR-PSO combines two groups of PSO with opposite acceleration coefficients. In addition, some particles are redistributed when they are trapped in local optima. Experimental studies on 5 benchmark functions with 50-dimensions and 100-dimensions show that the MGRR-PSO can solve the problems that can't be solved by original PSO with better performance.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Multi-group particle swarm optimization with random redistribution\",\"authors\":\"Naufal Suryanto, C. Ikuta, D. Pramadihanto\",\"doi\":\"10.1109/KCIC.2017.8228445\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Particle Swarm Optimization (PSO) is fast and popular algorithm to find the optimum value of non-linear and multi-dimensional function. However, it often easily trapped into local optima because the particles move closer to the best particle quickly. This paper purposes a new algorithm called Multi-Group Particle Swarm Optimization with Random Redistribution (MGRR-PSO) that tried to solve the weakness of standard PSO. MGRR-PSO combines two groups of PSO with opposite acceleration coefficients. In addition, some particles are redistributed when they are trapped in local optima. Experimental studies on 5 benchmark functions with 50-dimensions and 100-dimensions show that the MGRR-PSO can solve the problems that can't be solved by original PSO with better performance.\",\"PeriodicalId\":117148,\"journal\":{\"name\":\"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)\",\"volume\":\"55 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/KCIC.2017.8228445\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KCIC.2017.8228445","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 3

摘要

粒子群算法(PSO)是求解非线性、多维函数最优值的一种快速、流行的算法。然而，它往往很容易陷入局部最优，因为粒子更快地向最佳粒子靠近。本文提出了一种基于随机再分布的多群粒子群优化算法(MGRR-PSO)，试图解决标准粒子群优化算法的不足。MGRR-PSO结合了两组加速度系数相反的PSO。此外，当粒子被困在局部最优时，它们会重新分布。对50维和100维5个基准函数的实验研究表明，MGRR-PSO可以较好地解决原粒子群算法无法解决的问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Multi-group particle swarm optimization with random redistribution

Particle Swarm Optimization (PSO) is fast and popular algorithm to find the optimum value of non-linear and multi-dimensional function. However, it often easily trapped into local optima because the particles move closer to the best particle quickly. This paper purposes a new algorithm called Multi-Group Particle Swarm Optimization with Random Redistribution (MGRR-PSO) that tried to solve the weakness of standard PSO. MGRR-PSO combines two groups of PSO with opposite acceleration coefficients. In addition, some particles are redistributed when they are trapped in local optima. Experimental studies on 5 benchmark functions with 50-dimensions and 100-dimensions show that the MGRR-PSO can solve the problems that can't be solved by original PSO with better performance.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)

自引率

0.00%

发文量