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":"16 1","pages":"0"},"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.