{"title":"Lightweight approach for multi-objective web service composition","authors":"J. Liao, Yang Liu, Jing Wang, Jingyu Wang, Q. Qi","doi":"10.1049/iet-sen.2014.0155","DOIUrl":null,"url":null,"abstract":"Service composition is an efficient way to implement a service of complex business process in heterogeneous environment. Existing service selection methods mainly utilise fitness function or constraint technique to convert multiple objectives service composition problems to single objective ones. These methods need to take effect with priori knowledge of problem's solution space. Besides, in each execution only one solution can be obtained, hence, users can hardly acquire evenly distributed solutions with acceptable computation cost. The authors also propose a lightweight particle swarm optimisation service selection algorithm for multi-objective service composition problems. Simulation results illustrate that the proposed algorithm surpasses the comparative algorithm in approximation, coverage and execution time.","PeriodicalId":13395,"journal":{"name":"IET Softw.","volume":"67 1","pages":"116-124"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Softw.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1049/iet-sen.2014.0155","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Service composition is an efficient way to implement a service of complex business process in heterogeneous environment. Existing service selection methods mainly utilise fitness function or constraint technique to convert multiple objectives service composition problems to single objective ones. These methods need to take effect with priori knowledge of problem's solution space. Besides, in each execution only one solution can be obtained, hence, users can hardly acquire evenly distributed solutions with acceptable computation cost. The authors also propose a lightweight particle swarm optimisation service selection algorithm for multi-objective service composition problems. Simulation results illustrate that the proposed algorithm surpasses the comparative algorithm in approximation, coverage and execution time.