{"title":"引导局部搜索的MOEA/D:一些初步实验结果","authors":"Ahmad Alhindi, Qingfu Zhang","doi":"10.1109/CEEC.2013.6659455","DOIUrl":null,"url":null,"abstract":"Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) decomposes a multiobjective optimisation into a number of single-objective problem and optimises them in a collaborative manner. This paper investigates how to use the Guided Local Search (GLS), a well-studied single objective heuristic to enhance MOEA/D performance. In our proposed approach, the GLS applies to these subproblems to escape local Pareto optimal solutions. The experimental studies have shown that MOEA/D with GLS outperforms the classical MOEA/D on a bi-objective travelling salesman problem.","PeriodicalId":309053,"journal":{"name":"2013 5th Computer Science and Electronic Engineering Conference (CEEC)","volume":"158 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"MOEA/D with guided local search: Some preliminary experimental results\",\"authors\":\"Ahmad Alhindi, Qingfu Zhang\",\"doi\":\"10.1109/CEEC.2013.6659455\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) decomposes a multiobjective optimisation into a number of single-objective problem and optimises them in a collaborative manner. This paper investigates how to use the Guided Local Search (GLS), a well-studied single objective heuristic to enhance MOEA/D performance. In our proposed approach, the GLS applies to these subproblems to escape local Pareto optimal solutions. The experimental studies have shown that MOEA/D with GLS outperforms the classical MOEA/D on a bi-objective travelling salesman problem.\",\"PeriodicalId\":309053,\"journal\":{\"name\":\"2013 5th Computer Science and Electronic Engineering Conference (CEEC)\",\"volume\":\"158 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 5th Computer Science and Electronic Engineering Conference (CEEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEEC.2013.6659455\",\"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 5th Computer Science and Electronic Engineering Conference (CEEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEEC.2013.6659455","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
MOEA/D with guided local search: Some preliminary experimental results
Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) decomposes a multiobjective optimisation into a number of single-objective problem and optimises them in a collaborative manner. This paper investigates how to use the Guided Local Search (GLS), a well-studied single objective heuristic to enhance MOEA/D performance. In our proposed approach, the GLS applies to these subproblems to escape local Pareto optimal solutions. The experimental studies have shown that MOEA/D with GLS outperforms the classical MOEA/D on a bi-objective travelling salesman problem.
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