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Discrete Dynamics in Nature and Society
Volume 2012, Article ID 578064, 22 pages
Research Article

A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization

1Glorious Sun School of Business and Management, DongHua University, Shanghai 200051, China
2Computer Science and Technology Institute, University of South China, Hunan, Hengyang 421001, China
3Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong 643000, China

Received 24 April 2012; Revised 24 August 2012; Accepted 24 August 2012

Academic Editor: Gabriele Bonanno

Copyright © 2012 Daqing Wu and Jianguo Zheng. 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.


A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO) and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC) for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms.