Abstract
Particle swarm optimization (PSO) is a robust swarm intelligent technique inspired from birds flocking and fish schooling. Though many effective improvements have been proposed, however, the premature convergence is still its main problem. Because each particle’s movement is a continuous process and can be modelled with differential equation groups, a new variant, particle swarm optimization with dynamic step length (PSO-DSL), with additional control coefficient- step length, is introduced. Then the absolute stability theory is introduced to analyze the stability character of the standard PSO, the theoretical result indicates the PSO with constant step length can not always be stable, this may be one of the reason for premature convergence. Simulation results show the PSO-DSL is effective.
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Cui, Z., Cai, X., Zeng, J., Sun, G. (2007). Particle Swarm Optimization with Dynamic Step Length. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2007. Lecture Notes in Computer Science(), vol 4682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_81
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DOI: https://doi.org/10.1007/978-3-540-74205-0_81
Publisher Name: Springer, Berlin, Heidelberg
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