{"title":"Applying an evolutionary algorithm to compare fix and flex grid technologies in new generation optical networks","authors":"S. Kozdrowski, Pawel Krysztofik","doi":"10.1145/3583133.3590756","DOIUrl":null,"url":null,"abstract":"This paper proposes applying 2 algorithms to minimize network resources using fix-grid and flex-grid technology in next-generation optical networks. An algorithm based on the (μ + λ) evolutionary algorithm was proposed and compared with an exact method based on Mixed-Integer Linear Programming. The value of the objective function and the computation time was used as the primary metrics for performance evaluation. The performance of the proposed algorithms was investigated for a 10-node network with different node degrees from two to five. The presented results confirm the advantages of the proposed evolutionary approach, especially in terms of computational time, compared to the reference method.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3583133.3590756","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
This paper proposes applying 2 algorithms to minimize network resources using fix-grid and flex-grid technology in next-generation optical networks. An algorithm based on the (μ + λ) evolutionary algorithm was proposed and compared with an exact method based on Mixed-Integer Linear Programming. The value of the objective function and the computation time was used as the primary metrics for performance evaluation. The performance of the proposed algorithms was investigated for a 10-node network with different node degrees from two to five. The presented results confirm the advantages of the proposed evolutionary approach, especially in terms of computational time, compared to the reference method.