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Algorithms for Multipolar Interval-Valued Neutrosophic Soft Set with Information Measures to Solve Multicriteria
Rana Muhammad Zulqarnain1, Imran Siddique2, Aiyared Iampan3
1Department of Mathematics, School of Science, University of Management and Technology, Sialkot Campus, Lahore, Pakistan.
Computational Intelligence and Neuroscience
|November 22, 2021
Summary
This study introduces a new framework, m-polar interval-valued neutrosophic soft sets (mPIVNSS), to handle vague information. Novel similarity measures and correlation coefficients are developed for multicriteria decision-making problems.
Area of Science:
- Information Science
- Decision Science
- Fuzzy Set Theory
Background:
- Similarity measures and correlation coefficients address challenges with vague and imprecise information.
- Existing methods struggle with general vagueness and information overload.
Purpose of the Study:
- To propose an m-polar interval-valued neutrosophic soft set (mPIVNSS) by integrating m-polar fuzzy sets and interval-valued neutrosophic soft sets.
- To investigate operations, necessity/possibility, and weighted average operators for mPIVNSS.
- To develop novel similarity measures and correlation coefficients for mPIVNSS.
Main Methods:
- Defined mPIVNSS by merging m-polar fuzzy sets and interval-valued neutrosophic soft sets.
- Introduced AND, OR, truth-favorite, false-favorite, necessity, and possibility operations.
- Developed cosine and set-theoretic similarity measures using Bhattacharya distance.
- Extended correlation coefficient (CC) and weighted correlation coefficient (WCC) for mPIVNSS.
- Proposed three algorithms for multicriteria decision-making using mPIVNSS.
Main Results:
- Established properties of defined mPIVNSS operations and operators.
- Investigated fundamental properties of proposed similarity measures and CC/WCC for mPIVNSS.
- Demonstrated the effectiveness of three novel algorithms for multicriteria decision-making problems.
Conclusions:
- The proposed mPIVNSS framework offers a robust approach for handling complex, vague information.
- The developed similarity measures, correlation coefficients, and algorithms provide effective tools for decision-making.
- Comparative analysis highlights the advantages, effectiveness, and flexibility of the new techniques.
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