Abstract
Particle swarm optimization is a powerful algorithm that has been applied to various kinds of problems. However, it suffers from falling into local minimum and prematurity especially on multimodal function optimization problems. In this paper, a phased adaptive particle swarm optimization(PAPSO) is proposed to solve such problem. The process is divided into the initial particle pre-searching phase and the post-searching cooperative phase. In the post phase, the strategy of selecting randomly a certain number of particles for entering the reverse-learning is one of the most effective ways of escaping local stagnation. The illustrative example is provided to confirm the validity, as compared with the SPSO, Dynamic Inertia Weight PSO(PSO-W), and Tradeoff PSO(PSO-T) in terms of convergence speed and the ability of jumping out of the local optimal value. Simulation results confirm that the proposed algorithm is effective and feasible.
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Yu, H., Yang, F. (2012). A Phased Adaptive PSO Algorithm for Multimodal Function Optimization. In: Huang, DS., Jiang, C., Bevilacqua, V., Figueroa, J.C. (eds) Intelligent Computing Technology. ICIC 2012. Lecture Notes in Computer Science, vol 7389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31588-6_10
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DOI: https://doi.org/10.1007/978-3-642-31588-6_10
Publisher Name: Springer, Berlin, Heidelberg
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