An improved cooperative quantum-behaved particle swarm optimization

Particle swarm optimization (PSO) is a population-based stochastic optimization. Its parameters are easy to control, and it operates easily. But, the particle swarm optimization is a local convergence algorithm. Quantum-behaved particle swarm optimization (QPSO) overcomes this shortcoming, and outperforms original PSO. Based on classical QPSO, cooperative quantum-behaved particle swarm optimization (CQPSO) is present. This CQPSO, a particle firstly obtaining several individuals using Monte Carlo method and these individuals cooperate between them. In the experiments, five benchmark functions and six composition functions are used to test the performance of CQPSO. The results show that CQPSO performs much better than the other improved QPSO in terms of the quality of solution and computational cost.