Abstract
A hybrid algorithm based on particle swarm optimization(PSO) and artificial fish swarm algorithm(AFSA) is proposed. It combines the advantages of PSO and AFSA. The improved AFSA is introduced into PSO at the iteration. The following behavior and swarming behavior of AFSA are performed on two sub-swarms simultaneously. The proposed algorithm increases the variety of the population and improves the accuracy of the solution.
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© 2012 Springer-Verlag Berlin Heidelberg
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Jiang, J., Bo, Y., Song, C., Bao, L. (2012). Hybrid Algorithm Based on Particle Swarm Optimization and Artificial Fish Swarm Algorithm. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31346-2_68
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DOI: https://doi.org/10.1007/978-3-642-31346-2_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31345-5
Online ISBN: 978-3-642-31346-2
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