Abstract : Quantum-behaved particle swarm optimization (QPSO) algorithm is a global convergence guaranteed algorithms, which outperforms original PSO in search ability but has fewer parameters to control. But QPSO algorithm is to be easily trapped into local optima as a result of the rapid decline in diversity. So this paper describes diversity-controlled into QPSO (QPSO-DC) to enhance the diversity of particle swarm, and then improve the search ability of QPSO. The experiment results on benchmark functions show that QPSO-DC has stronger global search ability than QPSO and standard PSO.
Hongxiu Li; Matti Mäntymäki; Xianfeng Zhang. 13th Conference on e-Business, e-Services and e-Society (I3E), Nov 2014, Sanya, China. Springer, IFIP Advances in Information and Communication Technology, AICT-445, pp.132-143, 2014, Digital Services and Information Intelligence. <10.1007/978-3-662-45526-5_13>
https://hal.inria.fr/hal-01342138
Contributeur : Hal Ifip <>
Soumis le : mardi 5 juillet 2016 - 14:38:55
Dernière modification le : mercredi 6 juillet 2016 - 10:09:02
Haixia Long, Haiyan Fu, Chun Shi. Quantum-Behaved Particle Swarm Optimization Based on Diversity-Controlled. Hongxiu Li; Matti Mäntymäki; Xianfeng Zhang. 13th Conference on e-Business, e-Services and e-Society (I3E), Nov 2014, Sanya, China. Springer, IFIP Advances in Information and Communication Technology, AICT-445, pp.132-143, 2014, Digital Services and Information Intelligence. <10.1007/978-3-662-45526-5_13>. <hal-01342138>
