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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References
Angeline, P.J.: Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and performance Differences. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 601–610. Springer, Heidelberg (1998)
Van den Bergh, F., Engelbrecht, A.P.: A New Locally Convergent Particle Swarm Optimizer. In: 2002 IEEE International Conference on systems, Man and Cybernetics (2002)
Van den Bergh, F.: An Analysis of Particle Swarm Optimizers. PhD Thesis. University of Pretoria (November 2001)
Clerc, M.: The Swarm and Queen: Towards a Deterministic and Adaptive Particle Swarm Optimization. In: Proc. CEC 1999, pp. 1951–1957 (1999)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proc. IEEE Int’l Conference on Neural Networks, vol. IV, pp. 1942–1948. IEEE Service Center, Piscataway (1995)
Kennedy, J.: Small Swrlds and Mega-minds: Effects of Neighborhood Topology on Particle Swarm Performance. In: Proc. Congress on Evolutionary Computation 1999, pp. 1931–1938 (1999)
Riget, J., Besterstr, J.S.: A Diversity-guided Particle Swarm Optimizer-the ARPSO
Shi, Y., Eberhart, R.C.: A Modified Particle Swarm. In: Proc. 1998 IEEE International Conference on Evolutionary Computation, pp. 1945–1950 (1998)
Suganthan, P.N.: Particle Swarm Optimizer with Neighborhood Operator. In: Proc. 1999 Congress on Evolutionary Computation, pp. 1958–1962 (1999)
Sun, J., et al.: Particle Swarm Optimization with Particles Having Quantum Behavior. In: Proc. 2004 Congress on Evolutionary Computation, pp. 325–331 (2004)
Sun, J., et al.: A Global Search Strategy of Quantum-behaved Particle Swarm Optimization. In: Proc. 2004 IEEE Conference on Cybernetics and Intelligent Systems (2004)
Shi, Y., Eberhart, R.: Empirical study of particle swarm optimization. In: Proc. of Congress on Evolutionary Computation, pp. 1945–1950 (1999)
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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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