The Community for Technology Leaders
Pacific-Asia Workshop on Computational Intelligence and Industrial Application, IEEE (2008)
Dec. 19, 2008 to Dec. 20, 2008
ISBN: 978-0-7695-3490-9
pp: 109-113
ABSTRACT
Based on the previous introduced Quantum-behaved Particle Swarm Optimization (QPSO), in this paper, a revised novel QPSO with hybrid cooperative search is proposed. Taking full advantages of the characteristics of mutualism among swarms, the cooperative search is carried out to improve the diversity of the swarms, so as to help the system escape from local optima and converge to global optima. With the help of the cooperative search among different swarms, Hybrid Cooperative Quantum-behaved Particle Swarm Optimization (HCQPSO) makes the swarms more efficient in global search. The experimental results on test functions show that HCQPSO with hybrid cooperative search outperforms the QPSO. In addition, simulation results show the suitability of the proposed algorithm in terms of effectiveness and robustness.
INDEX TERMS
Particle Swarm Optimization, Quantum-behaved Particle Swarm Optimization, Hybrid Cooperative Search
CITATION
Songfeng Lu, Chengfu Sun, "Coevolutionary Quantum-Behaved Particle Swarm Optimization with Hybrid Cooperative Search", Pacific-Asia Workshop on Computational Intelligence and Industrial Application, IEEE, vol. 01, no. , pp. 109-113, 2008, doi:10.1109/PACIIA.2008.137
90 ms
(Ver 3.3 (11022016))