Performance Evaluation and Comparison of Multi-objective optimization Algorithms

Abstract

Multi-objective optimization is undoubtedly one field with many applications in real life situations and constitutes a highly active research area. In this paper, a comparison among high-performing multi-objective metaheuristics optimization algorithms is provided. For the comparison, three well-known multi-objective optimization algorithms and the Random Search algorithm are utilized on benchmark multi-objective optimization test families. Their results are compared with the use of two different metrics in order to be fully and effectively assessed. Their results are also discussed, and some future research points are proposed.