Particle Swarms Cooperative Optimizer

A particle swarms cooperative optimizer (PSCO) algorithm with two layers framework is proposed. Particle swarms are employed to search best solution in the solution space independently in the bottom layer, and a single swarm is employed in top layer. Sates of the particles of the top swarm are updated based on global best solution has been searched by all the particle swarms both in bottom and top layer. Both the particle numbers of the swarms and updating schemes of particle states are independence. A disturbance factor is added to a particle swarm optimizer (PSO) for improving PSO algorithms' performance. When the time of the current global best solution having not been updated is longer than the disturbance factor, the particles' velocities will be reset in order to force swarms getting out of locally minimizers. Three benchmark functions are used in experiments, and the experimental results show that the performances of PSCO are superior to that of classical PSO and fuzzy PSO and hybrid PSO.