{"title":"差分进化粒子群优化(DEEPSO):一个成功的混合算法","authors":"Vladimiro Miranda, Rui Alves","doi":"10.1109/BRICS-CCI-CBIC.2013.68","DOIUrl":null,"url":null,"abstract":"This paper explores, with numerical case studies, the performance of an optimization algorithm that is a variant of EPSO, the Evolutionary Particle Swarm Optimization method. EPSO is already a hybrid approach that may be seen as a PSO with self-adaptive weights or an Evolutionary Programming approach with a self-adaptive recombination operator. The new hybrid DEEPSO retains the self-adaptive properties of EPSO but borrows the concept of rough gradient from Differential Evolution algorithms. The performance of DEEPSO is compared to a well-performing EPSO algorithm in the optimization of problems of the fixed cost type, showing consistently better results in the cases presented.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"74","resultStr":"{\"title\":\"Differential Evolutionary Particle Swarm Optimization (DEEPSO): A Successful Hybrid\",\"authors\":\"Vladimiro Miranda, Rui Alves\",\"doi\":\"10.1109/BRICS-CCI-CBIC.2013.68\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper explores, with numerical case studies, the performance of an optimization algorithm that is a variant of EPSO, the Evolutionary Particle Swarm Optimization method. EPSO is already a hybrid approach that may be seen as a PSO with self-adaptive weights or an Evolutionary Programming approach with a self-adaptive recombination operator. The new hybrid DEEPSO retains the self-adaptive properties of EPSO but borrows the concept of rough gradient from Differential Evolution algorithms. The performance of DEEPSO is compared to a well-performing EPSO algorithm in the optimization of problems of the fixed cost type, showing consistently better results in the cases presented.\",\"PeriodicalId\":306195,\"journal\":{\"name\":\"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-09-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"74\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.68\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.68","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Differential Evolutionary Particle Swarm Optimization (DEEPSO): A Successful Hybrid
This paper explores, with numerical case studies, the performance of an optimization algorithm that is a variant of EPSO, the Evolutionary Particle Swarm Optimization method. EPSO is already a hybrid approach that may be seen as a PSO with self-adaptive weights or an Evolutionary Programming approach with a self-adaptive recombination operator. The new hybrid DEEPSO retains the self-adaptive properties of EPSO but borrows the concept of rough gradient from Differential Evolution algorithms. The performance of DEEPSO is compared to a well-performing EPSO algorithm in the optimization of problems of the fixed cost type, showing consistently better results in the cases presented.