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Pareto based multi-objective evolutionary algorithms face several problems when dealing with a large number of objectives. In this situation, almost all solutions become non-dominated and there is no pressure towards the Pareto Front. The use of Particle Swarm Optimization algorithm (PSO) in multi-objective problems grew in recent years. The PSO has been found very efficient in solve Multi-Objective Problems (MOPs) and several Multi-Objective Particle Swarm Optimization algorithms (MOPSO) have been proposed. This paper has the goal to study how PSO is affected when dealing with many-objective problems. Recently, some many-objective techniques have been proposed to avoid the deterioration of the search ability of multi-objective algorithms.
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