Quantum-Behaved Particle Swarm Optimization with Cooperative-Competitive Coevolutionary
Songfeng Lu, Chengfu Sun · 2008
Based on the previous introduced quantum-behaved particle swarm optimization (QPSO), in this paper, a revised QPSO with hybrid cooperative and competitive mechanism is proposed. The cooperative and competitive mechanism improves the diversity of the swarm, so as to help the system escape from local optima and converge to global optima. Take full advantages of the cooperative and competitive search among different swarms, cooperative competitive quantum-behaved particle swarm optimization (COQPSO) makes the swarms more efficient in global search. The experimental results on test functions show that COQPSO with cooperative and competitive mechanism outperforms the QPSO and even can search out the minimum value for some test functions.