A new particle swarm optimization for dynamic environments

Abstract

Dynamic optimization in which global optima and local optima change over time is always a hot research topic. It has been shown that particle swarm optimization works well facing into dynamic environments. From another hands, learning automata is considered as an intelligent tool (agent) which can learn what action is the best one interacting with its environment. The great deluge algorithm is also a search algorithm applied to optimization problems. All these algorithms have their special drawbacks and advantages. In this paper it is examined can the combination of these algorithms results in the better performance dealing with dynamic problems. Indeed a learning automaton is employed per each particle of the swarm to decide whether the corresponding particle updates its velocity (and consequently its position) considering the best global particle, the best local particle or the combination global and local particles. Water level in the deluge algorithm is used in the progress of the algorithm. Experimental results on different dynamic environments modeled by moving peaks benchmark show that the combination of these algorithms outperforms Particle Swarm Optimization (PSO) algorithm, Fast Multi-Swarm Optimization (FMSO) method, a similar particle swarm algorithm for dynamic environments, for all tested environments. © 2011 Springer-Verlag.