{"title":"混沌量子克隆多目标进化算法","authors":"She Xiangyang, Zhu Minghao","doi":"10.1109/ANTHOLOGY.2013.6784763","DOIUrl":null,"url":null,"abstract":"Based on the ergodicity of chaotic searching, the efficiency of quantum computing and antibody clonal selection theory of Artificial Immune System (AIS), Chaos Quantum Clonal Multiobjective Evolutionary Algorithm (CQCMEA) is proposed in this paper. The algorithm encodes the initial population by qubit, updates individual by quantum rotated gates with chaotic variables and maintains the distribution and diversity of solutions by crowding distance. Theoretical analysis and experimental simulation proved the effectiveness of the algorithm.","PeriodicalId":203169,"journal":{"name":"IEEE Conference Anthology","volume":"103 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Chaos Quantum Clonal Multiobjective Evolutionary Algorithm\",\"authors\":\"She Xiangyang, Zhu Minghao\",\"doi\":\"10.1109/ANTHOLOGY.2013.6784763\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on the ergodicity of chaotic searching, the efficiency of quantum computing and antibody clonal selection theory of Artificial Immune System (AIS), Chaos Quantum Clonal Multiobjective Evolutionary Algorithm (CQCMEA) is proposed in this paper. The algorithm encodes the initial population by qubit, updates individual by quantum rotated gates with chaotic variables and maintains the distribution and diversity of solutions by crowding distance. Theoretical analysis and experimental simulation proved the effectiveness of the algorithm.\",\"PeriodicalId\":203169,\"journal\":{\"name\":\"IEEE Conference Anthology\",\"volume\":\"103 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Conference Anthology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ANTHOLOGY.2013.6784763\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Conference Anthology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ANTHOLOGY.2013.6784763","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Based on the ergodicity of chaotic searching, the efficiency of quantum computing and antibody clonal selection theory of Artificial Immune System (AIS), Chaos Quantum Clonal Multiobjective Evolutionary Algorithm (CQCMEA) is proposed in this paper. The algorithm encodes the initial population by qubit, updates individual by quantum rotated gates with chaotic variables and maintains the distribution and diversity of solutions by crowding distance. Theoretical analysis and experimental simulation proved the effectiveness of the algorithm.
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