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.

中文翻译：

---

直觉模糊软集参数约简的新算法

直觉模糊软集（IFSS）是用于不确定性描述的有用数学工具之一，在现实世界中的决策问题中具有许多应用。但是，当这些决策问题涉及不太重要或多余的参数时，计算将变得更加复杂。为了解决这个问题，本文基于评估得分准则，研究了IFSS的参数约简问题。最初，我们开发了一种基于IFSS的决策新方法。然后，使用新的决策标准，我们提出了三种不同的算法来降低IFSS的参数，以满足决策者的不同需求。在不同方面，我们将提出的算法与Ghosh等人的算法进行了比较。从比较结果可以明显看出，所提出的算法在效率和适用性方面都比Ghosh等人的算法好得多。我们还对新算法进行了比较研究，以确定它们在不同情况下的可行性。最后，我们采用大学招聘问题来验证所提出算法在现实生活中的决策问题中的有效性。