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The Scientific World Journal
Volume 2014 (2014), Article ID 215472, 16 pages
Research Article

An Adaptive Hybrid Algorithm Based on Particle Swarm Optimization and Differential Evolution for Global Optimization

1China Institute of Manufacturing Development, Nanjing University of Information Science & Technology, Nanjing 210044, China
2Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China
3School of Mechanic and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China

Received 30 September 2013; Accepted 17 November 2013; Published 9 February 2014

Academic Editors: T. Chen, Q. Cheng, and J. Yang

Copyright © 2014 Xiaobing Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into local optima and lack the ability of jumping out of local optima. A novel adaptive hybrid algorithm based on PSO and DE (HPSO-DE) is formulated by developing a balanced parameter between PSO and DE. Adaptive mutation is carried out on current population when the population clusters around local optima. The HPSO-DE enjoys the advantages of PSO and DE and maintains diversity of the population. Compared with PSO, DE, and their variants, the performance of HPSO-DE is competitive. The balanced parameter sensitivity is discussed in detail.