Dual Sub-swarm Interaction QPSO Algorithm Based on Different Correlation Coefficients

摘要

A novel quantum-behaved particle swarm optimization (QPSO) algorithm, the Dual sub-swarm Interaction QPSO Algorithm Based on Different Correlation Coefficients (DCC-QPSO) is proposed by constructing me master-slave sub-swarms with different potential well centers. In the novel algorithm, the master sub-swarm and the slave sub-swarm have respective functinons during the evolution through different information processing strategies. The master sub-swarm is conducive to maintaining the population diversity and enhance global search ability of particles, otherwise the slave could accelerate the convergence rate and strengthen particles' local search ability. Experiment results show that DCC-QPSO outperforms the traditional QPSO algorithm regarding optimization of multimodal functions.