Multi-objective Particle Swarm Optimization: A Survey of the State-of-the-art
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Abstract
In the last decade, multi-objective Particle Swarm Optimization (MOPSO) has been observed as one of the most powerful optimization algorithms in solving Multi-objective Optimization Problems (MOPs). Nowadays, it is becoming increasingly clear that MOPSO can handle complex MOPs based on the competitive-cooperative framework. The goal of this paper is to provide a comprehensive review of 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 multi-objective optimization performance metrics. Even though some hybrid MOPSO methods show promising multi-objective 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.
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