{"title":"多目标优化的竞争-合作协同进化范式","authors":"C. Goh, K. Tan, A. Tay","doi":"10.1109/ISIC.2007.4450894","DOIUrl":null,"url":null,"abstract":"This paper proposes a new coevolutionary paradigm that hybridizes competitive and cooperative mechanisms observed in nature to solve multi-objective optimization problems. The main idea of cooperationist-competitive coevolution is to allow the decomposition process of the optimization problem to adapt and emerge rather than being hand designed and fixed at the start of the evolutionary optimization process. In particular, each species subpopulation will compete to represent a particular subcomponent of the multi-objective problem while the eventual winners will cooperate to evolve the better solutions. The effectiveness of the competitive-cooperation coevolutionary algorithm (COEA) is validated against various multi-objective evolutionary algorithms upon three benchmark problems characterized by different difficulties in local optimality, non-convexity and high-dimensionality.","PeriodicalId":184867,"journal":{"name":"2007 IEEE 22nd International Symposium on Intelligent Control","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"A Competitive-Cooperation Coevolutionary Paradigm for Multi-objective Optimization\",\"authors\":\"C. Goh, K. Tan, A. Tay\",\"doi\":\"10.1109/ISIC.2007.4450894\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a new coevolutionary paradigm that hybridizes competitive and cooperative mechanisms observed in nature to solve multi-objective optimization problems. The main idea of cooperationist-competitive coevolution is to allow the decomposition process of the optimization problem to adapt and emerge rather than being hand designed and fixed at the start of the evolutionary optimization process. In particular, each species subpopulation will compete to represent a particular subcomponent of the multi-objective problem while the eventual winners will cooperate to evolve the better solutions. The effectiveness of the competitive-cooperation coevolutionary algorithm (COEA) is validated against various multi-objective evolutionary algorithms upon three benchmark problems characterized by different difficulties in local optimality, non-convexity and high-dimensionality.\",\"PeriodicalId\":184867,\"journal\":{\"name\":\"2007 IEEE 22nd International Symposium on Intelligent Control\",\"volume\":\"81 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE 22nd International Symposium on Intelligent Control\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISIC.2007.4450894\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE 22nd International Symposium on Intelligent Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISIC.2007.4450894","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Competitive-Cooperation Coevolutionary Paradigm for Multi-objective Optimization
This paper proposes a new coevolutionary paradigm that hybridizes competitive and cooperative mechanisms observed in nature to solve multi-objective optimization problems. The main idea of cooperationist-competitive coevolution is to allow the decomposition process of the optimization problem to adapt and emerge rather than being hand designed and fixed at the start of the evolutionary optimization process. In particular, each species subpopulation will compete to represent a particular subcomponent of the multi-objective problem while the eventual winners will cooperate to evolve the better solutions. The effectiveness of the competitive-cooperation coevolutionary algorithm (COEA) is validated against various multi-objective evolutionary algorithms upon three benchmark problems characterized by different difficulties in local optimality, non-convexity and high-dimensionality.
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