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Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

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2083-2567
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A Big-Bang Big-Crunch Optimized General Type-2 Fuzzy Logic Approach for Multi-Criteria Group Decision Making

Syibrah Naim
  • The Computational Intelligence Centre School of Computer Science and Electronic Engineering University of Essex, United Kingdom
/ Hani Hagras
  • The Computational Intelligence Centre School of Computer Science and Electronic Engineering University of Essex, United Kingdom
Published Online: 2014-12-30 | DOI: https://doi.org/10.2478/jaiscr-2014-0009

Abstract

Multi-Criteria Group Decision Making (MCGDM) aims to find a unique agreement from a number of decision makers/users by evaluating the uncertainty in judgments. In this paper, we present a General Type-2 Fuzzy Logic based approach for MCGDM (GFLMCGDM). The proposed system aims to handle the high levels of uncertainties which exist due to the varying Decision Makers’ (DMs) judgments and the vagueness of the appraisal. In order to find the optimal parameters of the general type-2 fuzzy sets, we employed the Big Bang-Big Crunch (BB-BC) optimization. The aggregation operation in the proposed method aggregates the various DMs opinions which allow handling the disagreements of DMs’ opinions into a unique approval. We present results from an application for the selection of reading lighting level in an intelligent environment. We carried out various experiments in the intelligent apartment (iSpace) located at the University of Essex. We found that the proposed GFL-MCGDM effectively handle the uncertainties between the various decision makers which resulted in producing outputs which better agreed with the users’ decision compared to type 1 and interval type 2 fuzzy based systems.

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About the article

Published Online: 2014-12-30

Published in Print: 2013-04-01


Citation Information: Journal of Artificial Intelligence and Soft Computing Research, ISSN (Online) 2083-2567, DOI: https://doi.org/10.2478/jaiscr-2014-0009.

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© 2015. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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