2025, Vol. 10, Issue 1, Part B
Generalized aggregation operators based on t-norm operations for complex intuitionistic fuzzy sets
Author(s): Upendra Yadav and Dhrub Kumar Singh
Abstract: This paper presents a comprehensive study of generalized aggregation operators based on t-norm operations for complex intuitionistic fuzzy sets (CIFS). Intuitionistic fuzzy sets (IFS) provide a more flexible framework for handling uncertainty compared to traditional fuzzy sets by incorporating both membership and non-membership functions. However, when dealing with complex scenarios, the need for more robust aggregation methods arises to effectively combine information from multiple sources. We propose new generalized aggregation operators that utilize t-norm operations, which are essential for ensuring consistency in aggregation under uncertainty. These operators include the complex intuitionistic fuzzy weighted averaging (CIFWA) and complex intuitionistic fuzzy weighted geometric (CIFWG) operators, designed to handle both membership and non-membership values simultaneously. We also explore their properties, including idempotency, monotonicity, and boundedness, providing theoretical insights into their behavior. Numerical examples illustrate the application of these operators in decision-making processes, highlighting their utility in various real-world problems such as risk assessment, multi-criteria decision analysis, and complex decision-making under uncertainty. The proposed methods extend the capabilities of intuitionistic fuzzy sets by offering a more comprehensive approach to aggregation, enhancing the decision-making process in complex, uncertain environments.
Pages: 162-169 | Views: 22 | Downloads: 7Download Full Article: Click Here
How to cite this article:
Upendra Yadav, Dhrub Kumar Singh. Generalized aggregation operators based on t-norm operations for complex intuitionistic fuzzy sets. Int J Stat Appl Math 2025;10(1):162-169.