{"title":"An Improved Evolutionary Multiobjective Service Composition Algorithm","authors":"Hao Yin, Changsheng Zhang, Ying Guo, Bin Zhang","doi":"10.1109/ISCID.2013.74","DOIUrl":null,"url":null,"abstract":"Evolutionary multi-objective service composition optimizer (E3) is a recently proposed optimization framework for SLA-Aware service composition. It considers multiple SLAs simultaneously and produces a set of Pareto solutions. Two multi-objective genetic algorithms: E3-MOGA and Extreme-E3 provided by E3 have shown very good performance in comparison to NSGA-II. In this paper, an improved version of E3-MOGA, namely E3-IMOGA is proposed, which incorporates a fine-grained domination assignment value strategy. We evaluated our approach experimentally using dataset and compared with E3-MOGA and NSGA-II. It reveals promising results in terms of the quality of individuals and the time for finding all feasible individuals.","PeriodicalId":297027,"journal":{"name":"2013 Sixth International Symposium on Computational Intelligence and Design","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Sixth International Symposium on Computational Intelligence and Design","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCID.2013.74","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Evolutionary multi-objective service composition optimizer (E3) is a recently proposed optimization framework for SLA-Aware service composition. It considers multiple SLAs simultaneously and produces a set of Pareto solutions. Two multi-objective genetic algorithms: E3-MOGA and Extreme-E3 provided by E3 have shown very good performance in comparison to NSGA-II. In this paper, an improved version of E3-MOGA, namely E3-IMOGA is proposed, which incorporates a fine-grained domination assignment value strategy. We evaluated our approach experimentally using dataset and compared with E3-MOGA and NSGA-II. It reveals promising results in terms of the quality of individuals and the time for finding all feasible individuals.
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