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An improved cooperative particle swarm optimization and its application

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Abstract

A powerful cooperative evolutionary particle swarm optimization (PSO) algorithm based on two swarms with different behaviors to improve the global performance of PSO is proposed. In this method, one swarm tracks the best position and the other leaves the worst position of them; the best and the worst solutions of the two swarms are exchanged in the common blackboard and the information can be flowed mutually between them. The diversity is maintained if the two swarms are regarded as a whole. To show the effectiveness of the given algorithm, five benchmark functions and two forward ANNs with three layers are performed; the results of the proposed algorithms are compared with standard PSO, MCPSO and NPSO.

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Acknowledgments

This research was partially supported by the Natural Science Foundation of Anhui Province, China, Project No. 090412070 and Science Foundation for the Distinguished Young Researchers of Anhui Province, China, Project No. 2009SQRZ088ZD.

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Authors and Affiliations

  1. The School of Physics and Electronic Information, Huai Bei Normal University, 235000, Huaibei, China

    Debao Chen

  2. Computer Institute of NanJing University of Science and Technology, 210094, Nanjing, China

    Chunxia Zhao & Haofeng Zhang

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  1. Debao Chen
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  2. Chunxia Zhao
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  3. Haofeng Zhang
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Correspondence to Debao Chen.

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Chen, D., Zhao, C. & Zhang, H. An improved cooperative particle swarm optimization and its application. Neural Comput & Applic 20, 171–182 (2011). https://doi.org/10.1007/s00521-010-0503-4

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  • DOI: https://doi.org/10.1007/s00521-010-0503-4

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