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Combined with a variety of ideas a Multi-swarm cooperative Perturbed Particle Swarm Optimization algorithm (MpPSO) is presented to improve the performance and to reduce the premature convergence of PSO. This algorithm includes the idea of multiple swarms to improve the evolution efficiency by information sharing between populations to avoid falling into local optimum caused by single population. It also includes the idea of perturbing the swarms beside the global best solution, which can escape from local optimum. Experiments show that the proposed algorithm MpPSO has better performance, better convergence and stability when comparing with the traditional and the recently improved particle swarm optimization.

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