Investigation of Some Fuzzy Optimization Problems with Fuzzy Genetic Algorithms

Fuzzy optimization techniques have proven to be highly effective in the field of optimization, particularly in scenarios where decision-making processes are complex and influenced by uncertainty. These methods address vagueness and ambiguity by leveraging the principles of fuzzy logic, making them applicable across various domains such as economics, engineering, healthcare, and environmental management. Optimization techniques are essential for enhancing performance and efficiency in numerous industries. Among these, fuzzy logic provides a robust framework for handling uncertainties and imprecision commonly encountered in real-world problems. In this paper, we explore fuzzy genetic algorithms as a solution to certain fuzzy optimization problems. We demonstrate that this approach yields a reliable approximation of solutions for such problems. Additionally, we illustrate the application of this algorithm in three key areas: maximum fuzzy flow, fuzzy regression, and fuzzy controller design. The foundation of fuzzy genetic algorithms lies in the discretization of interval-based fuzzy subsets. These algorithms offer an innovative way to generate approximate solutions for fuzzy optimization problems where variables are arbitrary fuzzy subsets of specific intervals. This makes them versatile and applicable to a wide range of challenges.

کلیدواژه ها:

Genetic Algorithm ، fuzzy optimization ، Fuzzy linear programming ، Fuzzy Regression