Abstract
Quantum-behaved particle swarm optimization (QPSO) algorithm is a global-convergence-guaranteed algorithm, 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-maintained into QPSO (QPSO-DM) to enhance the diversity of particle swarm and then improve the search ability of QPSO. The experiment results on benchmark functions show that QPSO-DM has stronger global search ability than QPSO and standard PSO.
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Acknowledgments
This work is supported by National Natural Science Fund (No. 61163042), Higher School Scientific Research Project of Hainan Province (Hjkj2013-22), International Science and Technology Cooperation Program of China (2012DFA11270), and Hainan International Cooperation Key Project (GJXM201105).
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Long, Hx., Wu, Sl. (2014). Quantum-Behaved Particle Swarm Optimization with Diversity-Maintained. In: Cao, BY., Ma, SQ., Cao, Hh. (eds) Ecosystem Assessment and Fuzzy Systems Management. Advances in Intelligent Systems and Computing, vol 254. Springer, Cham. https://doi.org/10.1007/978-3-319-03449-2_21
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DOI: https://doi.org/10.1007/978-3-319-03449-2_21
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