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New algorithms for parameter reduction of intuitionistic fuzzy soft sets

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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.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant no. 61673011).

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Authors and Affiliations

  1. School of Science, Nanjing University of Science and Technology, Nanjing, 210094, China

    Abid Khan & Yuanguo Zhu

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  1. Abid Khan
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  2. Yuanguo Zhu
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Correspondence to Abid Khan.

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Communicated by Anibal Tavares de Azevedo.

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Khan, A., Zhu, Y. New algorithms for parameter reduction of intuitionistic fuzzy soft sets. Comp. Appl. Math. 39, 232 (2020). https://doi.org/10.1007/s40314-020-01279-4

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