Quantum-Behaved Particle Swarm Optimization Based on Diversity-Controlled

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.
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Communication dans un congrès
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〉
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Soumis le : mardi 5 juillet 2016 - 14:38:55
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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〉

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