A Hybrid Algorithm for Solving the Economic Dispatch Problem

Raul Silva Barros, O. Cortes, R. Lopes, Josenildo Costa da Silva
{"title":"A Hybrid Algorithm for Solving the Economic Dispatch Problem","authors":"Raul Silva Barros, O. Cortes, R. Lopes, Josenildo Costa da Silva","doi":"10.1109/BRICS-CCI-CBIC.2013.108","DOIUrl":null,"url":null,"abstract":"The purpose of this work is to apply a hybrid algorithm based on Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) for solving the problem of Economic Dispatch, which is based on supplying an energy demand, subjected to some restriction and reach out the best possible cost. Basically, we use the mutation operator from GAs aiming to explore regions in the search space that cannot be reached out by the canonical version of PSO. The new algorithm shows good results when applied to solve the cases based on 3, 13 and 20 generators, respectively. Our results are compared against the canonical PSO and other ones available in the literature.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.108","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

Abstract

The purpose of this work is to apply a hybrid algorithm based on Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) for solving the problem of Economic Dispatch, which is based on supplying an energy demand, subjected to some restriction and reach out the best possible cost. Basically, we use the mutation operator from GAs aiming to explore regions in the search space that cannot be reached out by the canonical version of PSO. The new algorithm shows good results when applied to solve the cases based on 3, 13 and 20 generators, respectively. Our results are compared against the canonical PSO and other ones available in the literature.
求解经济调度问题的混合算法
研究了一种基于粒子群算法(PSO)和遗传算法(GA)的混合算法,用于解决以满足一定的能源需求为基础,在一定的限制条件下,寻求最大可能成本的经济调度问题。基本上,我们使用来自GAs的突变算子,旨在探索规范版本的PSO无法到达的搜索空间区域。应用该算法分别求解了基于3个、13个和20个发电机的情况,取得了较好的效果。我们的结果与规范PSO和其他文献中可用的结果进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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