On the Velocity Update in Multi-Objective Particle Swarm Optimizers

Since its appearance, Particle Swarm Optimization (PSO) has become a very popular technique for solving optimization problems because of both its simplicity and its fast convergence properties. In the last few years there has been a variety of proposals for extending it to handle with multiples objectives. Although many of them keep the same properties of the original algorithm, they face difficulties when tackling the optimization of some multi-modal problems, i.e., those having more than one suboptimal front of solutions. Recent studies have shown that this disadvantage could be related to the velocity of the particles: uncontrolled high velocities may have no effect in particles movements. While many of the contributions on the specialized literature have focused on the selection of the leaders of the swarm, studies about different schemes for controlling the velocity of the particles are scarce in the multi-objective domain. In this work, we study different mechanisms in order to update the velocity of each particle with the idea of enhancing the search capabilities of multi-objective PSO algorithms. Our experiments show that some modifications help to overcoming the difficulties observed in previous proposals when dealing with hard optimization problems.