Multiobjective Particle Swarm Optimization: A Survey of the State-of-the-Art

In the last decade, multiobjective particle swarm optimization (MOPSO) has been observed as one of the most powerful optimization algorithms in solving multiobjective optimization problems (MOPs). Nowadays, it is becoming increasingly clear that MOPSO can handle with complex MOPs based on the competitive-cooperative framework. The goal of this paper is to provide a comprehensive review on MOPSO from the basic principles to hybrid evolutionary strategies. To offer the readers insights on the prominent developments of MOPSO, the key parameters on the convergence and diversity performance of MOPSO were analyzed to reflect the influence on the searching performance of particles. Then, the main advanced MOPSO methods were discussed, as well as the theoretical analysis of multiobjective optimization performance metrics. Even though some hybrid MOPSO methods show promising multiobjective optimization performance, there is much room left for researchers to improve further, in particular in terms of engineering applications. As a result, further in-depth studies are required. This paper should motivate evolutionary computation researchers to pay more attention to this practical yet challenging area.