Multi-Swarm Particle Swarm Optimization with an Adaptive Type Selection for Restarting Particles
Keiji Tatsumi, Takashi Yukami, Tetsuzo Tanino
The particle swarm optimization method (PSO) is one of popular metaheuristic methods for global optimization. Although the PSO is simple and shows a good performance of finding a desirable solution, it is reported that almost all particles sometimes converge to an undesirable local minimum for some problems. Thus, many kinds of improved methods have been proposed to keep the diversity of the search process. In this paper, we propose a novel multi-type swarm PSO which uses two kinds of particles and multiple swarms including either kind of particles. All particles in each swarm search for solutions independently where the exchange of information between different swarms is restricted for the extensive exploration. In addition, the proposed model has the restarting system of particles which initializes a particle with a sufficiently small velocity by resetting its velocity and position, and adaptively selects the kind of the particle according to which kind of particles contribute to improvement of the objective function value. Furthermore, through some numerical experiments, we verify the abilities of the proposed model.