Abstract
Swarm-diversity is an important factor influencing the global convergence of particle swarm optimization (PSO). In order to overcome the premature convergence, the paper introduced a negative feedback mechanism into particle swarm optimization and developed an adaptive PSO. The improved method takes advantage of the swarm-diversity to control the tuning of the inertia weight (PSO-DCIW), which in turn can adjust the swarm-diversity adaptively and contribute to a successful global search. The proposed PSO-DCIW was applied to some well-known benchmarks and compared with the other notable improved methods for PSO. The relative experimental results show PSO-DCIW is a robust global optimization method for the complex multimodal functions, which can improve the performance of the standard PSO and alleviate the premature convergence validly.
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Jie, J., Zeng, J., Han, C. (2006). Adaptive Particle Swarm Optimization with Feedback Control of Diversity. 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_9
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DOI: https://doi.org/10.1007/11816102_9
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