{"title":"A New Solution to Economic Emission Load Dispatch Using Immune Genetic Algorithm","authors":"Hong-da Liu, Zhong-li Ma, Sheng Liu, H. Lan","doi":"10.1109/ICCIS.2006.252299","DOIUrl":null,"url":null,"abstract":"This paper introduces a kind of immune genetic algorithm which regards objective function as antigen, solution as antibody and updates the population using evolutionary strategy. After economic emission load dispatch which belongs to multi-objective constrained optimization problems is discussed, main processes of immune genetic algorithm to solve this matter is given. Through tests a power system model with five coal-burning generating units, feasibility and validity of this algorithm is proved. And by comparing with genetic algorithm and Hopfield neural network, optimization and quick constringency of this algorithm to solve similar problems are proved","PeriodicalId":296028,"journal":{"name":"2006 IEEE Conference on Cybernetics and Intelligent Systems","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE Conference on Cybernetics and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCIS.2006.252299","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 17
Abstract
This paper introduces a kind of immune genetic algorithm which regards objective function as antigen, solution as antibody and updates the population using evolutionary strategy. After economic emission load dispatch which belongs to multi-objective constrained optimization problems is discussed, main processes of immune genetic algorithm to solve this matter is given. Through tests a power system model with five coal-burning generating units, feasibility and validity of this algorithm is proved. And by comparing with genetic algorithm and Hopfield neural network, optimization and quick constringency of this algorithm to solve similar problems are proved
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