Abstract
The particle swarm optimization algorithm is an algorithm to find optimal regions of complex spaces through the interaction of individuals. Convergence analysis and parameter selection in the particle swarm optimization algorithm have been discussed in [2] and [7]. In this paper, the particle swarm optimization algorithm is analyzed further by using standard results from the dynamic system theory. Thus, we derived graphical parameter guidelines from it. Finally, we analyze the convergence of the algorithm by some examples.
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Xiao, R., Li, B., He, X. (2007). The Particle Swarm: Parameter Selection and Convergence. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007. Communications in Computer and Information Science, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74282-1_45
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DOI: https://doi.org/10.1007/978-3-540-74282-1_45
Publisher Name: Springer, Berlin, Heidelberg
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