Abstract
The particle swarm optimization (PSO) algorithm presents a new way for finding optimal solutions of complex optimization problems. In this paper a modified particle swarm optimization algorithm is presented. We modify the PSO algorithm in some aspects. Firstly, a contractive factor is introduced to the position update equation, and the particles are limited in search region. A new strategy for updating velocity is then adopted, in which the velocity is weakened linearly. Thirdly, using an idea of intersecting two modified PSO algorithms. Finally, adding an item of integral control in the modified algorithm can improve its global search ability. Based on these strategies, we proposed a new PSO algorithm named crossed PSO algorithm. Simulation results show that the crossed PSO is superior to the original PSO algorithm and can get overall promising performance over a wide range of problems.
This work was supported by the National Natural Science Foundations of China (60171045, 60374063 and 60133010).
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© 2006 Springer-Verlag Berlin Heidelberg
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Chen, TB., Dong, YL., Jiao, YC., Zhang, FS. (2006). Crossed Particle Swarm Optimization Algorithm. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_123
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DOI: https://doi.org/10.1007/11881070_123
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45901-9
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