Multi-Objective Particle Swarm Optimisation Methods

Abstract

This study compares a number of selection regimes for the choosing of global best (gbest) and personal best (pbest) for swarm members in multi-objective particle swarm optimisation (MOPSO). Two distinct gbest selection techniques are shown to exist in the literature, those that do not restrict the selection of archive members and those with `distance' based gbest selection techniques. Theoretical justification for both of these approaches is discussed, in terms of the two types of search that these methods promote, and the potential problem of particle clumping in MOPSO is described. The popular pbest selection methods in the literature are also compared, and the ffect of the recently introduced turbulence term is viewed in terms of the additional search it promotes, across all parameter combinations. In light of the discussion, new avenues of MOPSO research are highlighted.