{"title":"A Type-2 Fuzzy Multi-Objective Multi-Chromosomal Optimisation for Capacity Planning within Telecommunication Networks","authors":"Lewis Veryard, H. Hagras, A. Conway, G. Owusu","doi":"10.1109/FUZZ45933.2021.9494391","DOIUrl":null,"url":null,"abstract":"In this paper, we present a novel Type-2 fuzzy multi-objective multi-chromosomal optimisation algorithm for capacity planning within telecommunication networks. The proposed system is compared to one of the most successful multi-objective optimisation algorithms which is NSGA-II. This comparison shows that in the capacity planning problems the proposed algorithm can produce a better solution front than NSGA-II in 80% - 93 % of cases. Additionally the use of Type-2 fuzzy logic produces a better solution front in 72% of cases when compared to using Type-1 fuzzy logic.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZ45933.2021.9494391","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
In this paper, we present a novel Type-2 fuzzy multi-objective multi-chromosomal optimisation algorithm for capacity planning within telecommunication networks. The proposed system is compared to one of the most successful multi-objective optimisation algorithms which is NSGA-II. This comparison shows that in the capacity planning problems the proposed algorithm can produce a better solution front than NSGA-II in 80% - 93 % of cases. Additionally the use of Type-2 fuzzy logic produces a better solution front in 72% of cases when compared to using Type-1 fuzzy logic.
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