Author(s): Ravi M

Abstract: Fuzzy Logic Optimization in Decision-Making Systems offers a powerful framework for handling uncertainty, imprecision, and nonlinearity in complex problem environments. This study explores how fuzzy logic, combined with optimization techniques such as Genetic Algorithms, Particle Swarm Optimization, and Fuzzy Linear Programming, enhances the adaptability and accuracy of intelligent decision-making models. By incorporating flexible membership functions, linguistic variables, and rule-based structures, fuzzy-optimized systems can interpret ambiguous data and generate more realistic solutions compared to traditional deterministic methods. The paper examines the theoretical foundations of fuzzy optimization, evaluates its performance across multi-criteria decision problems, and highlights its applications in healthcare, engineering, finance, supply chain operations, and autonomous systems. Findings underscore that integrating optimization methods with fuzzy inference significantly improves decision efficiency, model robustness, and interpretability. The study concludes that fuzzy logic optimization provides a resilient and scalable approach for next-generation intelligent decision-making systems in uncertain and dynamic contexts.

DOI: 10.22271/maths.2025.v10.i12a.2204

Pages: 14-21 | Views: 51 | Downloads: 6

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