New algorithms for parameter reduction of intuitionistic fuzzy soft sets
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New algorithms for parameter reduction of intuitionistic fuzzy soft sets Abid Khan1
· Yuanguo Zhu1
Received: 20 April 2020 / Revised: 12 July 2020 / Accepted: 27 July 2020 © SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 2020
Abstract The intuitionistic fuzzy soft set (IFSS) is one of the useful mathematical tools for uncertainty description and has many applications in real-world decision-making problems. However, the computations become more complex when these decision-making problems involve less important or redundant parameters. To solve this problem, in this paper, we study the problem of parameter reduction of IFSS based on evaluation score criteria. Initially, we developed a new approach to IFSS-based decision-making. Then using the new decision criteria, we propose three different algorithms for parameter reduction of IFSSs satisfying the different needs of decision-makers. We compare the proposed algorithms with Ghosh et al.’s algorithms in terms of different aspects. It is evident from the comparison results that the proposed algorithms are much better than Ghosh et al.’s algorithms in terms of efficiency and applicability. We also provide a comparative study among the new algorithms to decide their feasibilities in different situations. Finally, we take a university recruitment problem to verify the effectiveness of the proposed algorithms in real-life decision-making problems. Keywords Soft set · Intuitionistic fuzzy soft set · Redundant parameter · Parameter reduction · Algorithm · Decision-making Mathematics Subject Classification 03E99 · 90B50 · 03D15
1 Introduction The decision-making process exists in every aspect of our daily life. The quality of decisionmaking results directly leads to our judgment of things. Because the real world is complex and changeable, the relationship between things is also imprecise and fuzzy, which brings
Communicated by Anibal Tavares de Azevedo.
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Abid Khan [email protected]; [email protected] Yuanguo Zhu [email protected]
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School of Science, Nanjing University of Science and Technology, Nanjing 210094, China 0123456789().: V,-vol
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A. Khan, Y. Zhu
uncertainty to the smooth progress of decision-making process. So far, many mathematical theories have been developed to deal with uncertainty such as probability theory (Kolmogorov 2018), fuzzy set theory (Zadeh 1965), rough set theory (Pawlak 1982), and intuitionistic fuzzy set theory (Atanassov 1986). Molodtsov (1999) introduced soft set theory as a new mathematical tool that describes things from the perspective of parameterization. Due to the adequate parameterization tool, soft set theory presents a more clear and realistic description of the real-world objects, which makes it very easy to use in practice. Molodtsov’s soft set was applied to several directions such as game theory, Perron and Riemann integrations, smoothness of function, operation research, measurement theory, and probability theory. After Molodtsov, Maji et al. (2003) defined several ope
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