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A perturbation based chaotic particle swarm optimization using multi-type swarms

In order to improve the particle swarm optimization (PSO) method, which is a popular metaheuristic method for global optimization, we already proposed a PSO exploiting a chaotic dynamical system with sinusoidal perturbations, where chaotic and standard particles search for solutions cooperatively. In this paper, we propose multi-type swarms for the chaotic PSO which has three kinds of particles, the standard, chaotic and PS particles, and two kinds of best solutions, the global best and promising solutions: The chaotic particle searches for solutions chaotically and extensively in the feasible region to update the promising solution, while the standard particle executes the detail search around the global best solution which is updated by all particles. Moreover, PS particle searches for solutions in detail around the promising solution in the same way of the standard particle to inform the promising region found by the chaotic particles to the standard particles. Through computational experiments, we verify the performance of the proposed model by applying it to some global optimization problems.