Abstract
In standard particle swarm optimization, velocity information only provides a moving direction of each particle of the swarm, though it also can be considered as one point if there is no limitation restriction. Predicted-velocity particle swarm optimization is a new modified version using velocity and position to search the domain space equality. In some cases, velocity information may be effectively, but fails in others. This paper presents a game-theoretic approach for designing particle swarm optimization with a mixed strategy. The approach is applied to design a mixed strategy using velocity and position vectors. The experimental results show the mixed strategy can obtain the better performance than the best of pure strategy.
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© 2006 Springer-Verlag Berlin Heidelberg
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Cui, Z., Cai, X., Zeng, J., Sun, G. (2006). Predicted-Velocity Particle Swarm Optimization Using Game-Theoretic Approach. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence and Bioinformatics. ICIC 2006. Lecture Notes in Computer Science(), vol 4115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816102_16
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DOI: https://doi.org/10.1007/11816102_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37277-6
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