Solving Fuzzy Nonlinear Optimization Problems Using Evolutionary Algorithms
Nijalingappa Yogeesh · 2024
Fuzzy nonlinear optimization problems are prevalent in real-world applications, where the objective function, constraints, and decision variables are subject to uncertainty and vagueness. Due to their capacity to manage intricate and nonlinear optimization landscapes, evolutionary algorithms (EAs) have become a potent tool for resolving such issues. This chapter provides an overview of the state-of-the-art in solving fuzzy nonlinear optimization problems using EAs. Thischapter begins with a discussion of the fundamentals of fuzzy optimization and EAs, followed by a review of existing research on this topic. Common EAs, including genetic algorithms, particle swarm optimization, and differential evolution, are introduced, along with their adaptations for fuzzy optimization problems. The chapter includes several case studies demonstrating the effectiveness of EAs in solving fuzzy nonlinear optimization problems. Performance comparisons with other methods for solving fuzzy optimization problems are provided, highlighting the strengths and limitations of each approach. Finally, thischapter explores potential future research directions in this area. This chapter aims to provide practitioners and researchers with a comprehensive overview of EAs’ application in solving fuzzy nonlinear optimization problems.