Fuzzy Multi-Objective Optimization Models for Solving Complex Decision-Making Challenges

Abstract

The fuzzy multi-objective optimization has become an important area of study in intelligent decision systems and is utilized in formulating complex problems that are characterized by uncertainty, imprecision and conflicting objectives. It has wide used in the design of engineering and in managing resources and in data-driven decision support settings where an accurate modeling is often impractical. The paper allows the development of a sophisticated fuzzy multi-objective optimization model, incorporating adaptive membership functions, dynamic weighting techniques and an adaptive evolutionary search algorithm to efficiently trade of competing goals. The methodology includes fuzzy satisfaction modeling and Pareto-based evaluation to guarantee convergence as well as a diversity of solutions. It also has a structured constraint-handling system, to cope with uncertainty in system constraints. The standard measures are used to assess the output of the suggested modelling. Findings have shown that there is a substantial improvement over the existing methods, the Generational Distance is reduced between 0.030 and 0.018, There has been an increase in the Hypervolume between 0.91 and 0.95 as well as an improvement in the Fuzzy Satisfaction Index between 0.87 and 0.93. These advances exhibit high quality convergence, diversity, as well as decision satisfaction. The results support the hypothesis that the proposed structure is a powerful and effective tool to solve difficult real-world optimization problems under uncertainty.