A novel particle swarm optimizer with time-delay

Particle swarm optimization (PSO) is a relatively new population-based heuristic optimization technique. It has been widely applied to optimization problems for simplicity and capability of finding fairly good solutions rapidly. However, it may be trapped in local optima and fails to converge to global optimum. In this paper, the concept of time-delay is introduced into PSO to control the process of information diffusion and keep the particle diversity. Four time-delay schemes are proposed then. Experimental results verify their superiority both in robustness and efficiency. Conclusions are drawn in the end.