Abstract
Quantum particle swarm optimization algorithm (QPSO) is a good optimization technique combines the ideas of quantum computing. Quantum particle swarm optimization algorithm has been successfully applied in many research and application areas. But traditional QPSO is easy to fall into local optimum value and the convergence rate is slow. To solve these problems, an improved quantum particle swarm optimization algorithm is proposed and implemented in this paper. The experiments on high dimensional function optimization showed that the improved algorithm have more powerful global exploration ability.
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Huang, Z. (2014). Adaptive Mutation Behavior for Quantum Particle Swarm Optimization. In: Pan, L., Păun, G., Pérez-Jiménez, M.J., Song, T. (eds) Bio-Inspired Computing - Theories and Applications. Communications in Computer and Information Science, vol 472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45049-9_30
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DOI: https://doi.org/10.1007/978-3-662-45049-9_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45048-2
Online ISBN: 978-3-662-45049-9
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