Multi-Objective Optimization in Asynchronous Environments: An Evolutionary Game Theory Approach

This paper presents an innovative approach to solving complex multi-objective optimization problems through an asynchronous and distributed evolutionary game theory method. The proposed algorithm, an extension of the IMGAMO algorithm, optimizes individual criteria separately at varying computational speeds, thus significantly enhancing computational efficiency and adaptability. This unique structure enables independent criterion optimization, catering to real-world applications where different objectives demand varying computational resources. The algorithm’s effectiveness is validated against traditional synchronous evolutionary multi-objective optimization algorithms, showing superior performance in handling diverse, real-world problems efficiently. The results underline the potential of the asynchronous approach in providing high-quality Pareto fronts, thus offering robust solutions for complex optimization challenges.