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.
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Liu, J., Liu, H., Shen, W. (2007). Stability Analysis of Particle Swarm Optimization. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2007. Lecture Notes in Computer Science(), vol 4682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_82
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DOI: https://doi.org/10.1007/978-3-540-74205-0_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74201-2
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