{"title":"多目标组合优化的新思路:量子多智能体进化算法","authors":"Dongming Zhao, Fei Tao","doi":"10.1109/CISS.2009.5054684","DOIUrl":null,"url":null,"abstract":"Multi-objective combinatorial optimization (MOCO) problem is investigated in this paper. Combining the characters of agent and quantum-bit, a new idea, i.e., Quantum multi-agent evolutionary algorithms (QMAEA), for addressing MOCO problem is proposed. In QMAEA, each agent represented with quantum-bit is defined as a solution. Several operations such as evaluation-operation, competition-operation, mutation-operation, and local-evolution-Operation are introduced in QMAEA. The working flow of QMAEA is presented.","PeriodicalId":433796,"journal":{"name":"2009 43rd Annual Conference on Information Sciences and Systems","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A new idea for addressing multi-objective combinatorial optimization: Quantum multi-agent evolutionary algorithms\",\"authors\":\"Dongming Zhao, Fei Tao\",\"doi\":\"10.1109/CISS.2009.5054684\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Multi-objective combinatorial optimization (MOCO) problem is investigated in this paper. Combining the characters of agent and quantum-bit, a new idea, i.e., Quantum multi-agent evolutionary algorithms (QMAEA), for addressing MOCO problem is proposed. In QMAEA, each agent represented with quantum-bit is defined as a solution. Several operations such as evaluation-operation, competition-operation, mutation-operation, and local-evolution-Operation are introduced in QMAEA. The working flow of QMAEA is presented.\",\"PeriodicalId\":433796,\"journal\":{\"name\":\"2009 43rd Annual Conference on Information Sciences and Systems\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-03-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 43rd Annual Conference on Information Sciences and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CISS.2009.5054684\",\"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 43rd Annual Conference on Information Sciences and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISS.2009.5054684","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A new idea for addressing multi-objective combinatorial optimization: Quantum multi-agent evolutionary algorithms
Multi-objective combinatorial optimization (MOCO) problem is investigated in this paper. Combining the characters of agent and quantum-bit, a new idea, i.e., Quantum multi-agent evolutionary algorithms (QMAEA), for addressing MOCO problem is proposed. In QMAEA, each agent represented with quantum-bit is defined as a solution. Several operations such as evaluation-operation, competition-operation, mutation-operation, and local-evolution-Operation are introduced in QMAEA. The working flow of QMAEA is presented.
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