{"title":"A modified Pareto strength ant colony optimization algorithm for the multi-objective optimization problems","authors":"I.D.I.D. Ariyasingha, T. Fernando","doi":"10.1109/ICIAFS.2016.7946519","DOIUrl":null,"url":null,"abstract":"Ant colony optimization is a meta-heuristic that has been widely used for solving combinatorial optimization problems, and most real-world applications are concerned with multi-objective optimization problems. The Pareto strength ant colony optimization (PSACO) algorithm, which uses the concepts of Pareto optimality and also the domination concept, has been shown to be very effective in optimizing any number of objectives simultaneously. This paper modifies the PSACO algorithm to solve two combinatorial optimization problems: the travelling salesman problem (TSP); and the job-shop scheduling problem (JSSP). It uses the random-weight based method as an improvement. The proposed method achieved a better performance than the original PSACO algorithm for both combinatorial optimization problems and obtained well-distributed Pareto-optimal fronts.","PeriodicalId":237290,"journal":{"name":"2016 IEEE International Conference on Information and Automation for Sustainability (ICIAfS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Conference on Information and Automation for Sustainability (ICIAfS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIAFS.2016.7946519","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Ant colony optimization is a meta-heuristic that has been widely used for solving combinatorial optimization problems, and most real-world applications are concerned with multi-objective optimization problems. The Pareto strength ant colony optimization (PSACO) algorithm, which uses the concepts of Pareto optimality and also the domination concept, has been shown to be very effective in optimizing any number of objectives simultaneously. This paper modifies the PSACO algorithm to solve two combinatorial optimization problems: the travelling salesman problem (TSP); and the job-shop scheduling problem (JSSP). It uses the random-weight based method as an improvement. The proposed method achieved a better performance than the original PSACO algorithm for both combinatorial optimization problems and obtained well-distributed Pareto-optimal fronts.
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