Dynamic swarms in PSO-based multiobjective optimization

In this paper, a multiple swarms MOPSO (called DSMOPSO) in which the number of swarms is dynamically adjusted is proposed to solve for multiobjective optimization. Three novel ideas are introduced to DSMOPSO: the dynamic swarm strategy to allocate an appropriate number of swarms as needed and justified, the modified PSO update mechanism to better manage the convergence and communication among and within swarms, and objective space compression and expansion strategy to progressively exploit the objective space during different stages of search process. Compared with some state- of-the-art designs, the proposed algorithm shows competitive results in producing well extended and near optimum Pareto fronts.