Multi-Exemplar Particle Swarm Optimization

PSO and its variants have proven to be useful algorithms for tackling a wide range of optimization problems in recent decades. However, PSO and most of its variants only consider the influences caused by global best position and personal historical best position. Such a single way of influencing often leads to an issue of insufficient diversity of population, and further makes the algorithms be prone to falling into local optimum. In this paper, we propose a multi-exemplar particle swarm optimization (MEPSO) to deal with this issue. Specifically, each particle will choose the global best particle and the best companion particle as its exemplars, which brings more useful knowledge for particle update. To further describe the influences with respect to different exemplars, we define two influence coefficients inspired by mechanics. Such influence coefficients ensure that the best current experience is shared while enrich the diversity of population. Moreover, in the light of the distance between each particle and its best companion particle on each dimension, a variable-scale search is given in this paper to enhance the overall convergence ability. To verify the effectiveness of our algorithm, we conduct abundant experiments on all functions of CEC2013 test suite. The experimental results show that MEPSO performs better than 14 competitors in terms of comprehensive performance and achieves state-of-the-art results.