Fuzzy systems with multiple rule bases for selection of alternatives using TOPSIS

Abdul Yaakob, Alexander Gegov, Mohamed Bader-El-Den, Siti Fatimah Abdul Rahman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

This paper introduces a novel modification of the technique for ordering of preference by similarity to ideal solution (TOPSIS) method and uses a fuzzy system with multiple rule bases to solve multi-criteria decision making problems where both benefit and cost criteria are presented as subsystems. Thus, the decision maker evaluates the performance of each alternative for optimization and further observes the performance for both benefit and cost criteria. This approach improves significantly the transparency of the TOPSIS method while ensuring high effectiveness in comparison to established methods. To ensure practicality and effectiveness of the proposed method, a traded equity case study is considered. Furthermore, the ranking based on the proposed method is validated comparatively using spearman rho correlation. The proposed method
outperforms the existing TOPSIS methods in terms of ranking for the case study under consideration.
Original languageEnglish
Title of host publicationProceedings of the 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
PublisherIEEE
Pages2083-2090
Number of pages8
ISBN (Electronic)978-1509006267
ISBN (Print)978-1509006274
DOIs
Publication statusPublished - 10 Nov 2016
Event2016 IEEE World Congress on Computational Intelligence - Vancouver, Canada
Duration: 25 Jul 201629 Jul 2016

Conference

Conference2016 IEEE World Congress on Computational Intelligence
Abbreviated titleIEEE WCCI
Country/TerritoryCanada
CityVancouver
Period25/07/1629/07/16

Keywords

  • fuzzy systems
  • multiple rule bases
  • TOPSIS
  • multicriteria decision making
  • spearman rho correlation
  • traded equity

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