Particle swarm optimization with convergence speed controller for large-scale numerical optimization

Particle swarm optimization (PSO) has high convergence speed yet with its major drawback of premature convergence when solving large-scale optimization problems. We argue that it can be empowered by adaptively adjusting its convergence speed for the problems. In this paper, a convergence speed controller is proposed to improve the performance of PSO for large-scale optimization. As an additional operator of PSO, the controller is applied periodically and independently. It has two conditions and rules for adjusting the convergence speed of PSO, one for premature convergence and the other for slow convergence. The effectiveness of the PSO with convergence speed controller is evaluated by calculating the benchmark functions of CEC’2010. The numerical results indicate that the proposed controller helps PSO to keep a balance between convergence speed and swarm diversity during the optimization process. The results also support our argument that PSO can on average outperform other PSOs and cooperative coevolution methods for large-scale optimization when working with the convergence speed controller.