A Comparative Study on Constraint Handling for Solving Economic Dispatch by Evolutionary Algorithms

W. Nakawiro
{"title":"A Comparative Study on Constraint Handling for Solving Economic Dispatch by Evolutionary Algorithms","authors":"W. Nakawiro","doi":"10.1109/IEECON.2018.8712170","DOIUrl":null,"url":null,"abstract":"Evolutionary algorithm (EA) has been well accepted as a suitable tool for solving non-convex economic dispatch problems. However the major challenge is how to handle both equality and inequality constraints properly. The penalized fitness is commonly used to evaluate quality of candidate solutions. Moreover searching for feasible space is very difficult for a problem with large number of variables and number of constraints. This paper proposes a general framework which can be applied to any EA for handling constraints in ED problems. Four EAs namely differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO) and harmony search (HS) were selected to demonstrate effectiveness in solving a non-convex ED problem of a 15 unit power system. Simulation results reveal that with the proposed constraint handling all optimization algorithms converge to the identical result based on 20 independent trials.","PeriodicalId":6628,"journal":{"name":"2018 International Electrical Engineering Congress (iEECON)","volume":"16 1","pages":"1-4"},"PeriodicalIF":0.0000,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Electrical Engineering Congress (iEECON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEECON.2018.8712170","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Evolutionary algorithm (EA) has been well accepted as a suitable tool for solving non-convex economic dispatch problems. However the major challenge is how to handle both equality and inequality constraints properly. The penalized fitness is commonly used to evaluate quality of candidate solutions. Moreover searching for feasible space is very difficult for a problem with large number of variables and number of constraints. This paper proposes a general framework which can be applied to any EA for handling constraints in ED problems. Four EAs namely differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO) and harmony search (HS) were selected to demonstrate effectiveness in solving a non-convex ED problem of a 15 unit power system. Simulation results reveal that with the proposed constraint handling all optimization algorithms converge to the identical result based on 20 independent trials.
演化算法求解经济调度约束处理的比较研究
进化算法(EA)作为求解非凸经济调度问题的一种合适工具已被广泛接受。然而,主要的挑战是如何正确处理相等和不相等的约束。惩罚适应度通常用于评价候选解的质量。此外,对于具有大量变量和约束条件的问题,寻找可行空间是非常困难的。本文提出了一个通用的框架,该框架可以应用于任何EA来处理ED问题中的约束。选择微分进化(DE)、遗传算法(GA)、粒子群优化(PSO)和和谐搜索(HS)四种算法,验证了它们在求解15机组电力系统非凸ED问题中的有效性。仿真结果表明,在所提出的约束条件下,经过20次独立试验,所有优化算法收敛到相同的结果。
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