{"title":"A Multi-Objective Genetic Algorithm for Optimizing Highway Alignments","authors":"M. Jha, A. Maji","doi":"10.1109/MCDM.2007.369448","DOIUrl":null,"url":null,"abstract":"We develop a multi-objective approach to optimize 3-dimensional (3D) highway alignments using a genetic algorithm. Multi-objective genetic algorithms have been very popular for handling trade-offs among various objectives. The concept of Pareto optimally has been introduced in works and multi-objective genetic algorithms have been developed for this purpose. What we have found is that every problem is unique and there is no black box approach to implement multi-objective genetic algorithms in all problems. We implement the Pareto-optimality concept to develop a multi-objective genetic algorithm for the 3D highway alignment optimization problem on which we have worked for the last 10 years. We apply the multi-objective optimization approach to an example problem on which we had previously worked. The results suggest that the multi-objective approach has great promise for obtaining the best trade-off among various objectives to reach an optimal solution","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MCDM.2007.369448","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15
Abstract
We develop a multi-objective approach to optimize 3-dimensional (3D) highway alignments using a genetic algorithm. Multi-objective genetic algorithms have been very popular for handling trade-offs among various objectives. The concept of Pareto optimally has been introduced in works and multi-objective genetic algorithms have been developed for this purpose. What we have found is that every problem is unique and there is no black box approach to implement multi-objective genetic algorithms in all problems. We implement the Pareto-optimality concept to develop a multi-objective genetic algorithm for the 3D highway alignment optimization problem on which we have worked for the last 10 years. We apply the multi-objective optimization approach to an example problem on which we had previously worked. The results suggest that the multi-objective approach has great promise for obtaining the best trade-off among various objectives to reach an optimal solution