A Cooperative Dual-swarm PSO for dynamic optimization problems

Many practical applications are dynamic over time, which require optimization algorithms not only to converge to optimum as soon as possible but also to track the changing optimum. In this paper, a Cooperative Dual-swarm PSO (CDPSO) is proposed to deal with dynamic optimization problems. CDPSO adopts dual-swarm structure to keep swarm diversity and track the changing optimum. Fractional Global Best Formation technique is employed to construct artificial global bests which are potential to be better. Also an adaptive mutation operator is designed to maintain particle diversity. The experiments demonstrate that the proposed algorithm is effective and stable in dynamic environment.