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Multi-objective evolutionary algorithms (MOEA) are particularly suitable to solve real life problems, but they have some limitations when dealing with problems with many objectives, typically more than three. Recently, some many-objective techniques were proposed to avoid the deterioration of the search ability of Pareto dominance based MOEA for many-objective problems. This work applies the control of dominance area in two different Multi-objective Particle Swarm Optimization algorithms and investigates the influence of this technique in a cooperative-based framework. Besides, an empirical study is performed to identify if the many-objective technique increases the quality of the PSO algorithms for many-objective problems. The experimental results are compared applying some quality indicators and statistical test.