A Hybrid PSO Based on Dynamic Clustering for Global Optimization

li, hongru, hu, jinxing and Jiang, Shouyong (2018) A Hybrid PSO Based on Dynamic Clustering for Global Optimization. IFAC-PapersOnLine, 51 (18). pp. 269-274. ISSN 2405-8963

Full content URL: https://doi.org/10.1016/j.ifacol.2018.09.311

A Hybrid PSO Based on Dynamic Clustering for Global Optimization
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Particle swarm optimization is a population-based global search method, and known to suffer from premature convergence prior to discovering the true global minimizer for global optimization problems. Taking balance of local intensive exploitation and global exploration into account, a novel algorithm is presented in the paper, called dynamic clustering hybrid particle swarm optimization (DC-HPSO). In the method, particles are constantly and dynamically clustered into several groups (sub-swarms) corresponding to promising sub-regions in terms of similarity of their generalized particles. In each group, a dominant particle is chosen to take responsibility for local intensive exploitation, while the rest are responsible for exploration by maintaining diversity of the swarm. The simultaneous perturbation stochastic approximation (SPSA) is introduced into our work in order to guarantee the implementation of exploitation and the standard PSO is modified for exploration. The experimental results show the efficiency of the proposed algorithm in comparison with several other peer algorithms.

Keywords:Dynamic clustering, Modified PSO, Exploitation, exploration, Simultaneous perturbation stochastic approximation
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:38713
Deposited On:09 Jan 2020 09:12

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