MOPSOhv: A new hypervolume-based multi-objective particle swarm optimizer

This paper proposes a new hypervolume-based multi-objective particle swarm optimizer (called MOPSOhv) that uses an external archive to store the global nondominated solutions found during the evolutionary process. The proposed algorithm makes use of the hypervolume contribution of archived solutions for selecting global and personal leaders for each particle in the main swarm, and also as a mechanism for pruning the external archive when it is updated with new nondominated solutions. In order to increase the diversity when particles are updated in their positions, a mutation operator is used. The performance of the proposed algorithm is evaluated adopting standard test problems and indicators reported in the specialized literature, comparing its results with respect to those obtained by state-of-the-art multi-objective evolutionary algorithms. Our preliminary results indicate that our proposal is competitive with respect to state-of-the-art multi-objective evolutionary algorithms, being particularly suitable for solving many-objective optimization problems (i.e., problems having more than 3 objectives).