Wang Jing, Zhou Yongsheng, Yang Hao-xiong, Zhan-Ju Hao
{"title":"A Trade-off Pareto Solution Algorithm for Multi-objective Optimization","authors":"Wang Jing, Zhou Yongsheng, Yang Hao-xiong, Zhan-Ju Hao","doi":"10.1109/CSO.2012.34","DOIUrl":null,"url":null,"abstract":"Most optimization problems in real life are multi-objective optimization problems. The difficulity of multi-objective programming lies in the fact that the objectives are in conflict with each other and an improvement of one objective may lead to the reduction of other objectives. While achieving the global optimal in all objective at the same time is impossible. we use particle swarm optimization to improve the multi-objective patero solution and get the multi-objective trade-off patero optimal solutions. Numerical experiments show that our algorithms are effective, we can get multi-objective patero solutions set and multi-objective trade-offs patero optimal solution at the same time.","PeriodicalId":170543,"journal":{"name":"2012 Fifth International Joint Conference on Computational Sciences and Optimization","volume":"120 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Fifth International Joint Conference on Computational Sciences and Optimization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSO.2012.34","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Most optimization problems in real life are multi-objective optimization problems. The difficulity of multi-objective programming lies in the fact that the objectives are in conflict with each other and an improvement of one objective may lead to the reduction of other objectives. While achieving the global optimal in all objective at the same time is impossible. we use particle swarm optimization to improve the multi-objective patero solution and get the multi-objective trade-off patero optimal solutions. Numerical experiments show that our algorithms are effective, we can get multi-objective patero solutions set and multi-objective trade-offs patero optimal solution at the same time.