基于AMPSO算法的网络重构问题全局最优解

K. Kiran Kumar, N. Venkata Ramana, S. Kamakshaiah
{"title":"基于AMPSO算法的网络重构问题全局最优解","authors":"K. Kiran Kumar, N. Venkata Ramana, S. Kamakshaiah","doi":"10.1109/POWERCON.2012.6401322","DOIUrl":null,"url":null,"abstract":"This paper presents a new Adaptive Mutation Particle Swarm Optimization algorithm for minimization of losses of a Reconfigured Distribution Network. The tendency of solution being struck up to local optima or premature convergence as in the case of conventional PSO is thoroughly avoided using this new proposed technique. The algorithm is based on variance of population's fitness. During running time, the mutation probability is mainly based on variance of population's fitness and current optimal solution. The algorithm is tested on a standard IEEE 16 and 32 bus systems. The simulation results obtained and comparison with other popular existing methods is proving the effectiveness of this method.","PeriodicalId":176214,"journal":{"name":"2012 IEEE International Conference on Power System Technology (POWERCON)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Global optimal solution for network reconfiguration problem using AMPSO algorithm\",\"authors\":\"K. Kiran Kumar, N. Venkata Ramana, S. Kamakshaiah\",\"doi\":\"10.1109/POWERCON.2012.6401322\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a new Adaptive Mutation Particle Swarm Optimization algorithm for minimization of losses of a Reconfigured Distribution Network. The tendency of solution being struck up to local optima or premature convergence as in the case of conventional PSO is thoroughly avoided using this new proposed technique. The algorithm is based on variance of population's fitness. During running time, the mutation probability is mainly based on variance of population's fitness and current optimal solution. The algorithm is tested on a standard IEEE 16 and 32 bus systems. The simulation results obtained and comparison with other popular existing methods is proving the effectiveness of this method.\",\"PeriodicalId\":176214,\"journal\":{\"name\":\"2012 IEEE International Conference on Power System Technology (POWERCON)\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE International Conference on Power System Technology (POWERCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/POWERCON.2012.6401322\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Power System Technology (POWERCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/POWERCON.2012.6401322","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

提出了一种新的自适应突变粒子群优化算法,用于解决配电网重构后的损失最小化问题。该方法完全避免了传统粒子群算法的局部最优解或过早收敛的问题。该算法基于种群适应度的方差。在运行过程中,突变概率主要取决于种群适应度方差和当前最优解。该算法在标准IEEE 16和32总线系统上进行了测试。仿真结果以及与现有常用方法的比较验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Global optimal solution for network reconfiguration problem using AMPSO algorithm
This paper presents a new Adaptive Mutation Particle Swarm Optimization algorithm for minimization of losses of a Reconfigured Distribution Network. The tendency of solution being struck up to local optima or premature convergence as in the case of conventional PSO is thoroughly avoided using this new proposed technique. The algorithm is based on variance of population's fitness. During running time, the mutation probability is mainly based on variance of population's fitness and current optimal solution. The algorithm is tested on a standard IEEE 16 and 32 bus systems. The simulation results obtained and comparison with other popular existing methods is proving the effectiveness of this method.
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
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信