{"title":"基于邻域搜索的差分进化","authors":"Yuzhen Liu, Shoufu Li","doi":"10.1109/CINC.2010.5643890","DOIUrl":null,"url":null,"abstract":"In order to improve the ability of neighborhood search of differential evolutionary (DE) algorithm, we propose a new variant of DE with linear neighborhood search, called LiNDE, for global optimization problems (GOPs). LiNDE employs a linear combination of triple vectors taken randomly from evolutionary population. The main characteristics of LiNDE are less parameters and powerful neighborhood search ability. Experimental studies are carried out on a benchmark set, and the results show that LiNDE significantly improved the performance of DE.","PeriodicalId":227004,"journal":{"name":"2010 Second International Conference on Computational Intelligence and Natural Computing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Differential Evolution with Neighborhood Search\",\"authors\":\"Yuzhen Liu, Shoufu Li\",\"doi\":\"10.1109/CINC.2010.5643890\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to improve the ability of neighborhood search of differential evolutionary (DE) algorithm, we propose a new variant of DE with linear neighborhood search, called LiNDE, for global optimization problems (GOPs). LiNDE employs a linear combination of triple vectors taken randomly from evolutionary population. The main characteristics of LiNDE are less parameters and powerful neighborhood search ability. Experimental studies are carried out on a benchmark set, and the results show that LiNDE significantly improved the performance of DE.\",\"PeriodicalId\":227004,\"journal\":{\"name\":\"2010 Second International Conference on Computational Intelligence and Natural Computing\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Second International Conference on Computational Intelligence and Natural Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CINC.2010.5643890\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Second International Conference on Computational Intelligence and Natural Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CINC.2010.5643890","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In order to improve the ability of neighborhood search of differential evolutionary (DE) algorithm, we propose a new variant of DE with linear neighborhood search, called LiNDE, for global optimization problems (GOPs). LiNDE employs a linear combination of triple vectors taken randomly from evolutionary population. The main characteristics of LiNDE are less parameters and powerful neighborhood search ability. Experimental studies are carried out on a benchmark set, and the results show that LiNDE significantly improved the performance of DE.