Cultured differential particle swarm optimization for numerical optimization problems

A cultural algorithm based on differential particle swarm optimization (DPSO) is proposed in this paper. The main novel feature of this approach is the use of DPSO as a population space. DPSO is a hybrid-optimized algorithm based principally on the introduction of the differential variation mechanism into the PSO algorithm, which will maintain swarm diversity. The knowledge sources contained in the belief space of the cultural algorithm are specifically designed according to the DPSO evolution population. Different knowledge sources are used to influence the variation operator of DPSO, in order to enhance search ability and reduce the computational cost. The simulation results of typical function optimization problems show that the proposed algorithm has good optimization quality.