{"title":"单位承诺的基因互补遗传算法","authors":"Liu Maojun, Tong Tiaosheng","doi":"10.1109/ICEMS.2001.970758","DOIUrl":null,"url":null,"abstract":"This paper presents a modified genetic algorithm solution to the unit commitment problem (UCP), and constructs three kinds of genetic operators. To enhance convergence rate of the algorithm and prevent converging at a local optimal solution, a gene complementary technology is proposed and is applied to the modified genetic algorithm, which is called a gene complementary genetic algorithm (GCGA). Simulation results show that GCGA is a very efficient algorithm for solution to UCP.","PeriodicalId":143007,"journal":{"name":"ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"A gene complementary genetic algorithm for unit commitment\",\"authors\":\"Liu Maojun, Tong Tiaosheng\",\"doi\":\"10.1109/ICEMS.2001.970758\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a modified genetic algorithm solution to the unit commitment problem (UCP), and constructs three kinds of genetic operators. To enhance convergence rate of the algorithm and prevent converging at a local optimal solution, a gene complementary technology is proposed and is applied to the modified genetic algorithm, which is called a gene complementary genetic algorithm (GCGA). Simulation results show that GCGA is a very efficient algorithm for solution to UCP.\",\"PeriodicalId\":143007,\"journal\":{\"name\":\"ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501)\",\"volume\":\"47 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-08-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEMS.2001.970758\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEMS.2001.970758","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A gene complementary genetic algorithm for unit commitment
This paper presents a modified genetic algorithm solution to the unit commitment problem (UCP), and constructs three kinds of genetic operators. To enhance convergence rate of the algorithm and prevent converging at a local optimal solution, a gene complementary technology is proposed and is applied to the modified genetic algorithm, which is called a gene complementary genetic algorithm (GCGA). Simulation results show that GCGA is a very efficient algorithm for solution to UCP.
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