{"title":"多目标优化问题的改进Pareto强度蚁群优化算法","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":"{\"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}","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
摘要
蚁群算法是一种元启发式算法，被广泛用于解决组合优化问题，大多数实际应用涉及多目标优化问题。Pareto强度蚁群优化算法(Pareto strength ant colony optimization, PSACO)采用了Pareto最优和支配的概念，在同时优化任意数量的目标时非常有效。本文对PSACO算法进行了改进，以解决两个组合优化问题:旅行商问题(TSP);作业车间调度问题(JSSP)。它使用基于随机权重的方法作为改进。该方法在两个组合优化问题上都取得了比原PSACO算法更好的性能，并获得了分布良好的pareto最优前沿。
A modified Pareto strength ant colony optimization algorithm for the multi-objective optimization problems
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.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
