{"title":"Solving Shortest Capacitated Path Problem Using a Bi-Objective Heuristic Approach","authors":"C. Grosan, A. Abraham","doi":"10.1109/AMS.2007.96","DOIUrl":null,"url":null,"abstract":"The shortest capacitated path problem is a well known problem in the networking area, having a wide range of applications. In the shortest capacitated path problem, a traffic flow occurs from a source node to a destination node in a certain direction subject to a cost constraint. In this paper, a new approach for dealing with this problem is proposed. The proposed algorithm uses a special way to build valid solutions and an improvement technique to adjust the path. Some numerical experiments are performed using randomly generated networks having 25-200 nodes. Empirical results are compared with the results obtained using genetic algorithms which is an established technique for solving networking problems","PeriodicalId":198751,"journal":{"name":"First Asia International Conference on Modelling & Simulation (AMS'07)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"First Asia International Conference on Modelling & Simulation (AMS'07)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AMS.2007.96","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The shortest capacitated path problem is a well known problem in the networking area, having a wide range of applications. In the shortest capacitated path problem, a traffic flow occurs from a source node to a destination node in a certain direction subject to a cost constraint. In this paper, a new approach for dealing with this problem is proposed. The proposed algorithm uses a special way to build valid solutions and an improvement technique to adjust the path. Some numerical experiments are performed using randomly generated networks having 25-200 nodes. Empirical results are compared with the results obtained using genetic algorithms which is an established technique for solving networking problems