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基于复直觉模糊集的阿基米德海伦平均算子及其在决策问题中的应用

Archimedean Heronian mean operators based on complex intuitionistic fuzzy sets and their applications in decision-making problems.

作者信息

Ali Zeeshan, Emam Walid, Mahmood Tahir, Wang Haolun

机构信息

Department of Mathematics and Statistics, Riphah International University Islamabad, Pakistan.

Department of Statistics and Operations Research, Faculty of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia.

出版信息

Heliyon. 2024 Jan 24;10(3):e24767. doi: 10.1016/j.heliyon.2024.e24767. eCollection 2024 Feb 15.

Abstract

In this article, we derive the Archimedean aggregation operators for complex intuitionistic fuzzy sets, for this, first, we evaluate some Archimedean operational laws based on complex intuitionistic fuzzy values and then we discuss their special cases because the Archimedean norms are the general form of all existing norms, for instance, algebraic, Einstein, Hamacher, and Frank operational laws. Furthermore, we present the complex intuitionistic fuzzy Archimedean Heronian aggregation operator and complex intuitionistic fuzzy weighted Archimedean Heronian aggregation operator. Several special cases and the basic properties of the above-proposed operators are also diagnosed, because proposing the Heronian mean operators based on Archimedean norms are very challenging and complicated tasks, because of their features and structure. Additionally, a decision-making process is developed under the identified operators by using complex intuitionistic fuzzy information. Finally, we illustrate several examples to show the multi-attribute decision-making technique is more flexible than the prevailing works with the help of sensitive analysis between explored and certain prevailing works.

摘要

在本文中,我们推导了用于复直觉模糊集的阿基米德聚合算子。为此,首先,我们基于复直觉模糊值评估了一些阿基米德运算定律,然后讨论了它们的特殊情况,因为阿基米德范数是所有现有范数的一般形式,例如代数、爱因斯坦、哈马赫和弗兰克运算定律。此外,我们提出了复直觉模糊阿基米德海伦聚合算子和复直觉模糊加权阿基米德海伦聚合算子。还诊断了上述算子的几个特殊情况和基本性质,因为基于阿基米德范数提出海伦均值算子是非常具有挑战性和复杂的任务,这是由于它们的特征和结构所致。此外,利用复直觉模糊信息在已识别的算子下开发了一个决策过程。最后,我们通过探索的和某些现有主流方法之间的敏感性分析来说明几个例子,以表明多属性决策技术比现有主流方法更灵活。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f36/10873673/1591a19d110c/gr1.jpg

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