{"title":"Environmental economic load dispatch using multi-objective differential evolution algorithm","authors":"R. Sharma, P. Samantaray, D. Mohanty, P. Rout","doi":"10.1109/ICEAS.2011.6147132","DOIUrl":null,"url":null,"abstract":"The economic and environmental dispatch problem is formulated as a Non-linear constrained multi-objective problem with competing and non-commensurable objectives of fuel cost and emission. This paper presents a new multi-objective differential evolution algorithm. Initially, a non dominated sorting genetic algorithm is employed to obtain a set of pareto solutions followed by Multi-objective differential evolution algorithm and its corresponding set of pareto solution. The proposed algorithm has been tested on a forty unit test system to illustrate the analysis. The results demonstrate the capabilities of the proposed Multi-objective differential evolution technique to generate the set of well-distributed Pareto-optimal solutions and also reflects its superiority in terms of diversity of the pareto-optimal set. The simulation results obtained from the proposed approach are compared with the NSGA-II method.","PeriodicalId":273164,"journal":{"name":"2011 International Conference on Energy, Automation and Signal","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Energy, Automation and Signal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEAS.2011.6147132","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14
Abstract
The economic and environmental dispatch problem is formulated as a Non-linear constrained multi-objective problem with competing and non-commensurable objectives of fuel cost and emission. This paper presents a new multi-objective differential evolution algorithm. Initially, a non dominated sorting genetic algorithm is employed to obtain a set of pareto solutions followed by Multi-objective differential evolution algorithm and its corresponding set of pareto solution. The proposed algorithm has been tested on a forty unit test system to illustrate the analysis. The results demonstrate the capabilities of the proposed Multi-objective differential evolution technique to generate the set of well-distributed Pareto-optimal solutions and also reflects its superiority in terms of diversity of the pareto-optimal set. The simulation results obtained from the proposed approach are compared with the NSGA-II method.