A Hybrid Cooperative Co-evolution Particle Swarm Optimizer for Function Optimization

A novel cooperative co-evolution particle swarm optimizer advanced through bringing in quantum-behaved theory and simulated annealing(SA) method(HCPSO)is proposed in this paper, which makes full use advantages containing great global searching ability and diverse particles of SA and quantum-behaved theory. To illustrate the performance of this algorithm, which is compared with particle swarm optimizer(PSO) and cooperative co-evolutionary particle swarm optimizer(CPSO), eight testing functions are selected with different dimensions for experiment. Experimental results indicate that the HCPSO algorithm improves the search performance on the benchmark functions significantly, and shows the effectiveness of the algorithm to solve optimization problems.