Abstract
This paper explores how the particle swarm optimization algorithm works inside and how the values of β influence the behavior of the particle. According to Lyapunov Stability theorem, the stability of the PSO algorithm is analyzed. It is found that when β < 4 , the PSO algorithm is stable; when β > 4, the PSO algorithm is unstable; when β = 4, the PSO algorithm is sensitive to the initial value and the system is chaotic. The experiment validated the above conclusions. © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Liu, J., Liu, H., & Shen, W. (2007). Stability analysis of particle swarm optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4682 LNAI, pp. 781–790). Springer Verlag. https://doi.org/10.1007/978-3-540-74205-0_82
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