Abstract
The standard Particle Swarm Optimizer may prematurely converge on suboptimal solutions that are not even guaranteed to be local extrema. A new particle swarm optimizer, called stochastic PSO, which is guaranteed to convergence to the global optimization solution with probability one, is presented based on the analysis of the standard PSO. And the global convergence analysis is made using the F.Solis and R.Wets’ research results. Finally, several examples are simulated to show that SPSO is more efficient than the standard PSO.
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© 2004 Springer-Verlag Berlin Heidelberg
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Cui, Z., Zeng, J. (2004). A Guaranteed Global Convergence Particle Swarm Optimizer. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds) Rough Sets and Current Trends in Computing. RSCTC 2004. Lecture Notes in Computer Science(), vol 3066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25929-9_96
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DOI: https://doi.org/10.1007/978-3-540-25929-9_96
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22117-3
Online ISBN: 978-3-540-25929-9
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