Abstract
Particle Swarm Optimization (PSO) is a population-based evolutionary search technique, which has comparable performance with Genetic algorithm. The existing PSOs, however, are not global-convergence-guaranteed algorithms, because the evolution equation of PSO, make the particle only search in a finite sampling space. In [10,11], a Quantum-behaved Particle Swarm Optimization algorithm is proposed that outperforms traditional PSOs in search ability as well as having less parameter. This paper focuses on discussing how to select parameter when QPSO is practically applied. After the QPSO algorithm is described, the experiment results of stochastic simulation are given to show how the selection of the parameter value influences the convergence of the particle in QPSO. Finally, two parameter control methods are presented and experiment results on the benchmark functions testify their efficiency.
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Sun, J., Xu, W., Liu, J. (2005). Parameter Selection of Quantum-Behaved Particle Swarm Optimization. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_66
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DOI: https://doi.org/10.1007/11539902_66
Publisher Name: Springer, Berlin, Heidelberg
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