{"title":"基于多目标差分进化算法的环境经济负荷调度","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":"{\"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}","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}
Environmental economic load dispatch using multi-objective differential evolution algorithm
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.