Lei Song, Rong-Qiang Zeng, Yang Wang, Ming-Sheng Shang
{"title":"Solving bi-objective unconstrained binary quadratic programming problem with multi-objective path relinking algorithm","authors":"Lei Song, Rong-Qiang Zeng, Yang Wang, Ming-Sheng Shang","doi":"10.1109/FSKD.2016.7603188","DOIUrl":null,"url":null,"abstract":"This paper investigates a multi-objective path relinking algorithm in order to optimize a bi-objective unconstrained binary quadratic programming problem. In this algorithm, we integrate the path relinking techniques into hypervolume-based multi-objective optimization, where we propose a method to generate a path and select a set of non-dominated solutions from the generated path for further improvements. Experimental results show that the proposed algorithm is very effective compared with the original multi-objective optimization algorithms.","PeriodicalId":373155,"journal":{"name":"2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FSKD.2016.7603188","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
This paper investigates a multi-objective path relinking algorithm in order to optimize a bi-objective unconstrained binary quadratic programming problem. In this algorithm, we integrate the path relinking techniques into hypervolume-based multi-objective optimization, where we propose a method to generate a path and select a set of non-dominated solutions from the generated path for further improvements. Experimental results show that the proposed algorithm is very effective compared with the original multi-objective optimization algorithms.