{"title":"结合差分进化算法和基于生物地理的配电网重构优化算法","authors":"Jingwen Li, Jin-quan Zhao","doi":"10.1109/POWERCON.2012.6401351","DOIUrl":null,"url":null,"abstract":"A method combining differential evolution algorithm with biogeography-based optimization algorithm was proposed for distribution network reconfiguration with the objective of network loss minimum. In the solving processes, through simplifying the structure of distribution network topology and using the encoded mode, which based on the loop coding, the number of solutions, which can't keep the network radiating, was greatly reduced. The proposed optimization method combines the advantages of differential evolution algorithm and biogeography-based optimization algorithm. It effectively overcomes the defect of early-maturing, improves the search speed and increases the probability of the optimal solution. A typical example of 69 nodes case was simulated by using the proposed algorithm. The results show that the proposed method is efficient, rapidly convergent and having good stability.","PeriodicalId":176214,"journal":{"name":"2012 IEEE International Conference on Power System Technology (POWERCON)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Combining Differential Evolution Algorithm with biogeography-based optimization algorithm for reconfiguration of distribution network\",\"authors\":\"Jingwen Li, Jin-quan Zhao\",\"doi\":\"10.1109/POWERCON.2012.6401351\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A method combining differential evolution algorithm with biogeography-based optimization algorithm was proposed for distribution network reconfiguration with the objective of network loss minimum. In the solving processes, through simplifying the structure of distribution network topology and using the encoded mode, which based on the loop coding, the number of solutions, which can't keep the network radiating, was greatly reduced. The proposed optimization method combines the advantages of differential evolution algorithm and biogeography-based optimization algorithm. It effectively overcomes the defect of early-maturing, improves the search speed and increases the probability of the optimal solution. A typical example of 69 nodes case was simulated by using the proposed algorithm. The results show that the proposed method is efficient, rapidly convergent and having good stability.\",\"PeriodicalId\":176214,\"journal\":{\"name\":\"2012 IEEE International Conference on Power System Technology (POWERCON)\",\"volume\":\"112 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE International Conference on Power System Technology (POWERCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/POWERCON.2012.6401351\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Power System Technology (POWERCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/POWERCON.2012.6401351","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Combining Differential Evolution Algorithm with biogeography-based optimization algorithm for reconfiguration of distribution network
A method combining differential evolution algorithm with biogeography-based optimization algorithm was proposed for distribution network reconfiguration with the objective of network loss minimum. In the solving processes, through simplifying the structure of distribution network topology and using the encoded mode, which based on the loop coding, the number of solutions, which can't keep the network radiating, was greatly reduced. The proposed optimization method combines the advantages of differential evolution algorithm and biogeography-based optimization algorithm. It effectively overcomes the defect of early-maturing, improves the search speed and increases the probability of the optimal solution. A typical example of 69 nodes case was simulated by using the proposed algorithm. The results show that the proposed method is efficient, rapidly convergent and having good stability.