Abstract
A simple way is proposed to estimate the non-negative real parameter tuple {ω, c1, c2} of standard Particle Swarm Optimization algorithm using control theory. The distribution of complex characteristic roots on the convergence region of particles is studied by means of linear discrete-time system analysis method. It is pointed out that the critical factors affecting the modulus value and the phase angle of the complex characteristic roots are the maximum overshoot and angular frequency of damped oscillation. The way shows that the product of the maximum overshoot and the angular frequency of damped oscillation approximately equaling to 1 is the promising guideline for parameter selection in PSO when the angular frequency in the range of (0.65π, 0.35π). Based on this, widely used benchmark problems are employed in series experiments using a stochastic approximation technique, and the results are well back above deduction.
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References
Clerc, M., Kennedy, J.: The particle swarm: explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002)
Trelea, I.C.: The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters 85, 317–325 (2003)
Jiang, M., Luo, Y.P., Yang, S.Y.: Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm. Information Processing Letters 102, 8–16 (2007)
Jiang, M., Luo, Y.P., Yang, S.Y.: Particle Swarm Optimization-Stochastic Trajectory Analysis and Parameter Selection (2007), Retrieved from www.i-techonline.com
Fernández Martínez, J.L., García Gonzalo, E.: The Generalized PSO: A New Door to PSO Evolution. Journal of Artificial Evolution and Applications, Article ID: 861275, 15 pages (2008), doi:10.1155/2008/861275
Zhang, W., Li, H., Zhao, Q., Wang, H.: Guidelines for Parameter Selection in Particle Swarm Optimization According to Control Theory. In: 2009 Fifth International Conference on Natural Computation (ICNC 2009), Tianjin, China, vol. 3, pp. 520–524 (2009)
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© 2011 Springer-Verlag Berlin Heidelberg
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Zhang, W., Jin, Y., Li, X., Zhang, X. (2011). A Simple Way for Parameter Selection of Standard Particle Swarm Optimization. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23896-3_54
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DOI: https://doi.org/10.1007/978-3-642-23896-3_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23895-6
Online ISBN: 978-3-642-23896-3
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