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Discrete Dynamics in Nature and Society
Volume 2017, Article ID 5193013, 22 pages
Research Article

Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History

1Department of Information Service & Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2University of Chinese Academy of Sciences, Beijing 100039, China
3Shenyang University, Shenyang 110044, China
4School of Computer Science and Software, Tianjin Polytechnic University, Tianjin 300387, China

Correspondence should be addressed to Maowei He; moc.liamtoh@iewoameh and Hanning Chen; moc.liamtoh@nhc_tcefrep

Received 4 November 2016; Revised 11 April 2017; Accepted 19 April 2017; Published 27 June 2017

Academic Editor: Seenith Sivasundaram

Copyright © 2017 Danping Wang 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.


A hybrid coevolution particle swarm optimization algorithm with dynamic multispecies strategy based on -means clustering and nonrevisit strategy based on Binary Space Partitioning fitness tree (called MCPSO-PSH) is proposed. Previous search history memorized into the Binary Space Partitioning fitness tree can effectively restrain the individuals’ revisit phenomenon. The whole population is partitioned into several subspecies and cooperative coevolution is realized by an information communication mechanism between subspecies, which can enhance the global search ability of particles and avoid premature convergence to local optimum. To demonstrate the power of the method, comparisons between the proposed algorithm and state-of-the-art algorithms are grouped into two categories: 10 basic benchmark functions (10-dimensional and 30-dimensional), 10 CEC2005 benchmark functions (30-dimensional), and a real-world problem (multilevel image segmentation problems). Experimental results show that MCPSO-PSH displays a competitive performance compared to the other swarm-based or evolutionary algorithms in terms of solution accuracy and statistical tests.