{"title":"蚁群优化中参数自适应的模糊系统","authors":"Frumen Olivas, F. Valdez, O. Castillo","doi":"10.1109/SIS.2014.7011780","DOIUrl":null,"url":null,"abstract":"In this paper we propose a fuzzy system for parameter adaptation in ant colony optimization (ACO). ACO is a method inspired in the behavior of ant colonies to find food and its objective are discrete optimization problems. We developed various fuzzy systems for parameter adaptation and in this paper a comparison was made between them. The use of a fuzzy system is to control the diversity of the solutions, this is, control the ability of exploration and exploitation of the ant colony.","PeriodicalId":380286,"journal":{"name":"2014 IEEE Symposium on Swarm Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"A fuzzy system for parameter adaptation in ant colony optimization\",\"authors\":\"Frumen Olivas, F. Valdez, O. Castillo\",\"doi\":\"10.1109/SIS.2014.7011780\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we propose a fuzzy system for parameter adaptation in ant colony optimization (ACO). ACO is a method inspired in the behavior of ant colonies to find food and its objective are discrete optimization problems. We developed various fuzzy systems for parameter adaptation and in this paper a comparison was made between them. The use of a fuzzy system is to control the diversity of the solutions, this is, control the ability of exploration and exploitation of the ant colony.\",\"PeriodicalId\":380286,\"journal\":{\"name\":\"2014 IEEE Symposium on Swarm Intelligence\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE Symposium on Swarm Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIS.2014.7011780\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Symposium on Swarm Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIS.2014.7011780","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A fuzzy system for parameter adaptation in ant colony optimization
In this paper we propose a fuzzy system for parameter adaptation in ant colony optimization (ACO). ACO is a method inspired in the behavior of ant colonies to find food and its objective are discrete optimization problems. We developed various fuzzy systems for parameter adaptation and in this paper a comparison was made between them. The use of a fuzzy system is to control the diversity of the solutions, this is, control the ability of exploration and exploitation of the ant colony.
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