Abstract
The paper proposes a modified particle swarm optimizer for tracking dynamic systems. In the new algorithm, the changed local optimum and global optimum are introduced to guide the movement of each particle and avoid making direction and velocity decisions on the basis of the outdated information. An environment influence factor is put forward based on the two optimums above, which dynamically decide the change of the inertia weight. The combinations of the different local optimum update strategy and local inertia weight update strategy are tested on the parabolic benchmark function. The results on the benchmark function with various severities suggest that modified particle swarm optimizer performs better in convergence speed and aggregation accuracy.
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Zhang, X., Du, Y., Qin, Z., Qin, G., Lu, J. (2005). A Modified Particle Swarm Optimizer for Tracking Dynamic Systems. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_72
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DOI: https://doi.org/10.1007/11539902_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28320-1
Online ISBN: 978-3-540-31863-7
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