{"title":"基于Pareto前沿和不动点理论的改进多目标遗传算法","authors":"Jingjun Zhang, Yanmin Shang, Ruizhen Gao, Yuzhen Dong","doi":"10.1109/IWISA.2009.5072719","DOIUrl":null,"url":null,"abstract":"For multi-objective optimization problems, an improved multi-objective genetic algorithm based on Pareto Front and Fixed Point Theory is proposed in this paper. In this Algorithm, the fixed point theory is introduced to multi-objective optimization questions and K1 triangulation is carried on to solutions for the weighting function constructed by all subfunctions, so the optimal problems are transferred to fixed point problems. The non-dominated-set is constructed by the method of exclusion. The experimental results show that this improved genetic algorithm convergent faster and is able to achieve a broader distribution of the Pareto optimal solution. Keywords— multi-objective optimization; Pareto Front; nondominated set; genetic algorithm; fixed point; K1 triangulation","PeriodicalId":6327,"journal":{"name":"2009 International Workshop on Intelligent Systems and Applications","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2009-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An Improved Multi-Objective Genetic Algorithm Based On Pareto Front and Fixed Point Theory\",\"authors\":\"Jingjun Zhang, Yanmin Shang, Ruizhen Gao, Yuzhen Dong\",\"doi\":\"10.1109/IWISA.2009.5072719\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For multi-objective optimization problems, an improved multi-objective genetic algorithm based on Pareto Front and Fixed Point Theory is proposed in this paper. In this Algorithm, the fixed point theory is introduced to multi-objective optimization questions and K1 triangulation is carried on to solutions for the weighting function constructed by all subfunctions, so the optimal problems are transferred to fixed point problems. The non-dominated-set is constructed by the method of exclusion. The experimental results show that this improved genetic algorithm convergent faster and is able to achieve a broader distribution of the Pareto optimal solution. Keywords— multi-objective optimization; Pareto Front; nondominated set; genetic algorithm; fixed point; K1 triangulation\",\"PeriodicalId\":6327,\"journal\":{\"name\":\"2009 International Workshop on Intelligent Systems and Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Workshop on Intelligent Systems and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IWISA.2009.5072719\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Workshop on Intelligent Systems and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWISA.2009.5072719","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Improved Multi-Objective Genetic Algorithm Based On Pareto Front and Fixed Point Theory
For multi-objective optimization problems, an improved multi-objective genetic algorithm based on Pareto Front and Fixed Point Theory is proposed in this paper. In this Algorithm, the fixed point theory is introduced to multi-objective optimization questions and K1 triangulation is carried on to solutions for the weighting function constructed by all subfunctions, so the optimal problems are transferred to fixed point problems. The non-dominated-set is constructed by the method of exclusion. The experimental results show that this improved genetic algorithm convergent faster and is able to achieve a broader distribution of the Pareto optimal solution. Keywords— multi-objective optimization; Pareto Front; nondominated set; genetic algorithm; fixed point; K1 triangulation
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