{"title":"网络最优重构的实参数遗传算法","authors":"R. A. Ah King, B. Radha, H. Rughooputh","doi":"10.1109/ICIT.2003.1290231","DOIUrl":null,"url":null,"abstract":"Distribution network reconfiguration belongs to a complex combinatorial optimization problem with multiple constraints. Solutions produced by heuristic search techniques often produce local optima. To overcome such a problem, a real-parameter genetic algorithm (GA) is used for solving the distribution network reconfiguration problem. Simulation results are presented for three test systems to demonstrate the applicability of the algorithm. Moreover, the effects of load variations on a practical network are also analyzed to determine the optimal configuration.","PeriodicalId":193510,"journal":{"name":"IEEE International Conference on Industrial Technology, 2003","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"A real-parameter genetic algorithm for optimal network reconfiguration\",\"authors\":\"R. A. Ah King, B. Radha, H. Rughooputh\",\"doi\":\"10.1109/ICIT.2003.1290231\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Distribution network reconfiguration belongs to a complex combinatorial optimization problem with multiple constraints. Solutions produced by heuristic search techniques often produce local optima. To overcome such a problem, a real-parameter genetic algorithm (GA) is used for solving the distribution network reconfiguration problem. Simulation results are presented for three test systems to demonstrate the applicability of the algorithm. Moreover, the effects of load variations on a practical network are also analyzed to determine the optimal configuration.\",\"PeriodicalId\":193510,\"journal\":{\"name\":\"IEEE International Conference on Industrial Technology, 2003\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE International Conference on Industrial Technology, 2003\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIT.2003.1290231\",\"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 International Conference on Industrial Technology, 2003","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIT.2003.1290231","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A real-parameter genetic algorithm for optimal network reconfiguration
Distribution network reconfiguration belongs to a complex combinatorial optimization problem with multiple constraints. Solutions produced by heuristic search techniques often produce local optima. To overcome such a problem, a real-parameter genetic algorithm (GA) is used for solving the distribution network reconfiguration problem. Simulation results are presented for three test systems to demonstrate the applicability of the algorithm. Moreover, the effects of load variations on a practical network are also analyzed to determine the optimal configuration.
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