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The interest in the application of particle swarm optimization to solve different problems, especially multi-objective problems, grew in recent years. This metaheuristic is particularly suitable to solve real life problems, but like other multi-objective metaheuristics, has 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 multi-objective evolutionary algorithms for many-objective problems. This work presents a study of the influence of the many-objective technique called the control of dominance area of solutions (CDAS) in multi-objective particle swarm optimization. It is presented an empirical analysis to identify the influence of the CDAS technique on the convergence and the diversity of a multi-objective PSO algorithm in many-objective scenarios through the analysis of some quality indicators and statistical tests.