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An improved cooperative quantum-behaved particle swarm optimization

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Abstract

Particle swarm optimization (PSO) is a population-based stochastic optimization. Its parameters are easy to control, and it operates easily. But, the particle swarm optimization is a local convergence algorithm. Quantum-behaved particle swarm optimization (QPSO) overcomes this shortcoming, and outperforms original PSO. Based on classical QPSO, cooperative quantum-behaved particle swarm optimization (CQPSO) is present. This CQPSO, a particle firstly obtaining several individuals using Monte Carlo method and these individuals cooperate between them. In the experiments, five benchmark functions and six composition functions are used to test the performance of CQPSO. The results show that CQPSO performs much better than the other improved QPSO in terms of the quality of solution and computational cost.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Nos. 61001202 and 60803098), the Provincial Natural Science Foundation of Shaanxi of China (Nos. 2009JQ8015, 2010JM8030 and 2010JQ8023), the China Postdoctoral Science Foundation Funded Project (Nos. 20080431228, 20090461283 and 20090451369), the China Postdoctoral Science Foundation Special Funded Project (No. 200801426), the Fundamental Research Funds for the Central Universities (Nos. JY10000902040, JY10000902039, JY10000903007 and K50510020011), and the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) (No. B07048).

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Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi’an, 710071, China

    Yangyang Li, Rongrong Xiang, Licheng Jiao & Ruochen Liu

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  1. Yangyang Li
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  2. Rongrong Xiang
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  3. Licheng Jiao
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  4. Ruochen Liu
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Correspondence to Yangyang Li.

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Li, Y., Xiang, R., Jiao, L. et al. An improved cooperative quantum-behaved particle swarm optimization. Soft Comput 16, 1061–1069 (2012). https://doi.org/10.1007/s00500-012-0803-y

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