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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Long, H., Fu, H., Shi, C. (2014). Quantum-Behaved Particle Swarm Optimization Based on Diversity-Controlled. In: Li, H., Mäntymäki, M., Zhang, X. (eds) Digital Services and Information Intelligence. I3E 2014. IFIP Advances in Information and Communication Technology, vol 445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45526-5_13
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DOI: https://doi.org/10.1007/978-3-662-45526-5_13
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