{"title":"城市群优化","authors":"Yijun Yang, H. Duan","doi":"10.1109/CHICC.2015.7260034","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a novel swarm intelligence optimization algorithm-city group optimization (CGO). CGO loosely mimics the evolution of city group. The basic components of CGO include road network, position updating rules, and transportation hub updating. These components are inspired by the evolutionary phenomena in city group. The detailed implementation procedure is also given. Series of comparative experiments on six benchmark functions with particle swarm optimization (PSO) are conducted, and the results verify the feasibility and effectiveness of our proposed CGO in solving continuous optimization problems.","PeriodicalId":421276,"journal":{"name":"2015 34th Chinese Control Conference (CCC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"City group optimization\",\"authors\":\"Yijun Yang, H. Duan\",\"doi\":\"10.1109/CHICC.2015.7260034\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a novel swarm intelligence optimization algorithm-city group optimization (CGO). CGO loosely mimics the evolution of city group. The basic components of CGO include road network, position updating rules, and transportation hub updating. These components are inspired by the evolutionary phenomena in city group. The detailed implementation procedure is also given. Series of comparative experiments on six benchmark functions with particle swarm optimization (PSO) are conducted, and the results verify the feasibility and effectiveness of our proposed CGO in solving continuous optimization problems.\",\"PeriodicalId\":421276,\"journal\":{\"name\":\"2015 34th Chinese Control Conference (CCC)\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-07-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 34th Chinese Control Conference (CCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CHICC.2015.7260034\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 34th Chinese Control Conference (CCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CHICC.2015.7260034","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we propose a novel swarm intelligence optimization algorithm-city group optimization (CGO). CGO loosely mimics the evolution of city group. The basic components of CGO include road network, position updating rules, and transportation hub updating. These components are inspired by the evolutionary phenomena in city group. The detailed implementation procedure is also given. Series of comparative experiments on six benchmark functions with particle swarm optimization (PSO) are conducted, and the results verify the feasibility and effectiveness of our proposed CGO in solving continuous optimization problems.
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