{"title":"蜂群优化:原理与应用","authors":"D. Teodorovic, P. Lucic, G. Marković, M. D. Orco","doi":"10.1109/NEUREL.2006.341200","DOIUrl":null,"url":null,"abstract":"The bee colony optimization metaheuristic (BCO) is proposed in the paper. The BCO represents the new metaheuristic capable to solve difficult combinatorial optimization problems. The artificial bee colony behaves partially alike, and partially differently from bee colonies in nature. In addition to proposing the BCO as a new metaheuristic, we also describe in the paper two BCO algorithms that we call the bee system (BS) and the fuzzy bee system (FBS). In the case of FBS the agents (artificial bees) use approximate reasoning and rules of fuzzy logic in their communication and acting. In this way, the FBS is capable to solve deterministic combinatorial problems, as well as combinatorial problems characterized by uncertainty. The proposed approach is illustrated by three case studies","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"194","resultStr":"{\"title\":\"Bee Colony Optimization: Principles and Applications\",\"authors\":\"D. Teodorovic, P. Lucic, G. Marković, M. D. Orco\",\"doi\":\"10.1109/NEUREL.2006.341200\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The bee colony optimization metaheuristic (BCO) is proposed in the paper. The BCO represents the new metaheuristic capable to solve difficult combinatorial optimization problems. The artificial bee colony behaves partially alike, and partially differently from bee colonies in nature. In addition to proposing the BCO as a new metaheuristic, we also describe in the paper two BCO algorithms that we call the bee system (BS) and the fuzzy bee system (FBS). In the case of FBS the agents (artificial bees) use approximate reasoning and rules of fuzzy logic in their communication and acting. In this way, the FBS is capable to solve deterministic combinatorial problems, as well as combinatorial problems characterized by uncertainty. The proposed approach is illustrated by three case studies\",\"PeriodicalId\":231606,\"journal\":{\"name\":\"2006 8th Seminar on Neural Network Applications in Electrical Engineering\",\"volume\":\"54 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"194\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 8th Seminar on Neural Network Applications in Electrical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NEUREL.2006.341200\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NEUREL.2006.341200","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bee Colony Optimization: Principles and Applications
The bee colony optimization metaheuristic (BCO) is proposed in the paper. The BCO represents the new metaheuristic capable to solve difficult combinatorial optimization problems. The artificial bee colony behaves partially alike, and partially differently from bee colonies in nature. In addition to proposing the BCO as a new metaheuristic, we also describe in the paper two BCO algorithms that we call the bee system (BS) and the fuzzy bee system (FBS). In the case of FBS the agents (artificial bees) use approximate reasoning and rules of fuzzy logic in their communication and acting. In this way, the FBS is capable to solve deterministic combinatorial problems, as well as combinatorial problems characterized by uncertainty. The proposed approach is illustrated by three case studies
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