MPSO: Median-oriented Particle Swarm Optimization

Abstract

Particle Swarm Optimization (PSO) is a bio-inspired optimization algorithm which has been empirically demonstrated to perform well on many optimization problems. However, it has two main weaknesses which have restricted the wider applications of PSO. The algorithm can easily get trapped in the local optima and has slow convergence speed. Therefore, improvement and/or elimination of these disadvantages are the most important objective in PSO research. In this paper, we propose Median-oriented Particle Swarm Optimization (MPSO) to carry out a global search over entire search space with accelerating convergence speed and avoiding local optima. The median position of particles and the worst and median fitness values of the swarm are incorporated in the standard PSO to achieve the mentioned goals. The proposed algorithm is evaluated on 20 unimodal, multimodal, rotated and shifted high-dimensional benchmark functions and the results are compared with some well-known PSO algorithms in the literature. The results show that MPSO substantially enhances the performance of the PSO paradigm in terms of convergence speed and finds global or good near-global optimal in the functions.