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Discrete Dynamics in Nature and Society
Volume 2010, Article ID 379649, 30 pages
Research Article

Hierarchical Swarm Model: A New Approach to Optimization

1Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Faculty Office III, Nanta Street 114#, Dongling District, Shenyang 110016, China
2School of Information Science and Engineering, Central South University, Changsha 410083, China

Received 1 September 2009; Revised 16 January 2010; Accepted 8 March 2010

Academic Editor: Aura Reggiani

Copyright © 2010 Hanning Chen 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.


This paper presents a novel optimization model called hierarchical swarm optimization (HSO), which simulates the natural hierarchical complex system from where more complex intelligence can emerge for complex problems solving. This proposed model is intended to suggest ways that the performance of HSO-based algorithms on complex optimization problems can be significantly improved. This performance improvement is obtained by constructing the HSO hierarchies, which means that an agent in a higher level swarm can be composed of swarms of other agents from lower level and different swarms of different levels evolve on different spatiotemporal scale. A novel optimization algorithm (named P S 2 O ), based on the HSO model, is instantiated and tested to illustrate the ideas of HSO model clearly. Experiments were conducted on a set of 17 benchmark optimization problems including both continuous and discrete cases. The results demonstrate remarkable performance of the P S 2 O algorithm on all chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms.