A prospect theory-based MABAC algorithm with novel similarity measures and interactional operations for picture fuzzy sets and its applications

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Article number106787
Journal / PublicationEngineering Applications of Artificial Intelligence
Volume126
Issue numberPart A
Online published22 Jul 2023
Publication statusPublished - Nov 2023

Abstract

Picture fuzzy set (PFS) is one the reliable tool to handle the uncertainties in the data as compared to the intuitionistic fuzzy set (IFS) or fuzzy set. PFS simultaneously handle the four degrees namely, membership, neutrality, non-membership, and refusal, and thus widely applicable to solve the real-life decision-making problems more accurately. Keeping their advantages, in this paper, we present some interactive operational laws for the picture fuzzy numbers (PFNs) to aggregate picture fuzzy information. Also, we state some new information measures namely picture fuzzy similarity measures (PFSimMs) based on fuzzy strict negations, which can overcome the various drawbacks of the existing PFSimMs. The various properties and their features are studied in detail to show their advantages. Finally, we develop a prospect theory-based multi-attributive border approximation area comparison (MABAC) method under picture fuzzy environment by using the proposed operational laws and PFSimMs to solve the decision-making problems. The applicability of the developed algorithm is explained through a numerical example and show its superiorities. © 2023 Elsevier Ltd.

Research Area(s)

  • Interactional operation, Medical diagnosis, Multiple attribute decision-making, Pattern recognition, Picture fuzzy set, Similarity measure

Citation Format(s)

A prospect theory-based MABAC algorithm with novel similarity measures and interactional operations for picture fuzzy sets and its applications. / Wang, Tao; Wu, Xinxing; Garg, Harish et al.
In: Engineering Applications of Artificial Intelligence, Vol. 126, No. Part A, 106787, 11.2023.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review