期刊
APPLIED SOFT COMPUTING
卷 10, 期 2, 页码 629-640出版社
ELSEVIER
DOI: 10.1016/j.asoc.2009.08.031
关键词
Particle swarm optimization; Differential evolution; Constrained optimization; PSO-DE
We propose a novel hybrid algorithm named PSO-DE, which integrates particle swarm optimization (PSO) with differential evolution (DE) to solve constrained numerical and engineering optimization problems. Traditional PSO is easy to fall into stagnation when no particle discovers a position that is better than its previous best position for several generations. DE is incorporated into update the previous best positions of particles to force PSO jump out of stagnation, because of its strong searching ability. The hybrid algorithm speeds up the convergence and improves the algorithm's performance. We test the presented method on 11 well-known benchmark test functions and five engineering optimization functions. Comparisons show that PSO-DE outperforms or performs similarly to seven state-of-the-art approaches in terms of the quality of the resulting solutions. (C) 2009 Elsevier B.V. All rights reserved.
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