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An improved cooperative particle swarm optimizer

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Abstract

Particle Swarm Optimization (PSO) is a population-based technique for optimization, which simulates the social behavior of the bird flocking, a novel Adaptive Cooperative PSO (ACPSO) with adaptive search is presented in this paper, the proposed approach combines both cooperative learning and PSO with adaptive inertia weight, cooperative learning is achieved by splitting a high-dimensional swarm into several smaller-dimensional subswarms to combat curse of dimensionality, the adaptive inertia weight is employed to control the balance of exploration and exploitation in all the smaller-dimensional subswarms, which cooperate with each other by exchanging information to determine composite fitness of the entire system. Finally, computer simulations over three benchmarks indicate that the proposed algorithm shows better convergence behavior, as compared to the Cooperative Genetic Algorithm (COGA), the PSO, and the CPSO, and then its adaptive search behavior is analyzed, demonstrating its superiority.

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References

  1. Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proc. IEEE international conference on neural networks (Vol. 4, pp. 1942–1948).

    Chapter  Google Scholar 

  2. Afsahi, Z., & Meybodi, M. R. (2009). Improving cooperative PSO using fuzzy logic. In SGAI conf. (pp. 219–232).

    Google Scholar 

  3. Maitra, M., & Chatterjee, A. (2008). A hybrid cooperative-comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding. Expert Systems with Applications, 34(2), 1341–1350.

    Article  Google Scholar 

  4. El-Abd, M., & Kamel, M. S. (2008). A taxonomy of cooperative particle swarm optimizers. International Journal of Computational Intelligence Research, 4(2), 137–144.

    Article  Google Scholar 

  5. Gzara, F., & Erkut, E. (2011). Telecommunications network design with multiple technologies. Telecommunications Systems, 46(2), 149–161.

    Article  Google Scholar 

  6. Du, Y., Wu, Q., Jiang, C., & Li, Z. (2008). Improved cooperative particle swarm optimizer for design of fuzzy neural network control system. Control and Decision, 23(12), 1327–1337.

    Google Scholar 

  7. Li, M., Li, W., & Yang, C.-w. (2010). An improved cooperative PSO algorithm. In Proceedings of the 8th World Congress on intelligent control and automation (pp. 3256–3260).

    Google Scholar 

  8. Eberhart, R. C., & Shi, Y. (2000). Comparing inertia weights and constriction factors in particle swarm optimization. In Proceedings of the Congress on evolutionary computation (pp. 84–88).

    Google Scholar 

  9. Clerc, M., & Kennedy, J. (2002). The particle swarm-explosion, stability and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6, 58–73.

    Article  Google Scholar 

  10. El-Zonkoly, A. (2005). Particle swarm optimization for solving the problem of transmission systems and generation expansion. Nansoura Engineering, 30(1), 201–206.

    Google Scholar 

  11. Zhao, W., Kang, Y., Pan, G., & Huang, X. (2001). Fault diagnosis of power transformer based on BP combined with genetic algorithm. Communications in Computer and Information Science, 134, 33–38.

    Article  Google Scholar 

  12. Reza, F., Abdul, T., Zaiton, S., & Ngah, R. (2007). New particle swarm optimizer with sigmoid increasing inertia weight. International Journal of Computer Science and Security, 1(2), 35–44.

    Google Scholar 

  13. Liu, B., Wang, L., & Jin, Y. H. (2005). Improved particle swarm optimization combined with chaos. Chaos, Solitons and Fractals, 25, 1261–1271.

    Article  Google Scholar 

  14. Ramakrishnan, S., & Emary, I. M. (2011). Classification brain MR images through a fuzzy multiwavelets based GMM and probabilistic neural networks. Telecommunications Systems, 46(3), 245–252.

    Article  Google Scholar 

  15. Zhao, W., Xia, Z., & Chang, X. (2011). An improved bacterial foraging optimization with fuzzy step size. Information, 14(3), 725–730.

    Google Scholar 

  16. Potter, M. A., & de Jong, K. A. (1994). A cooperative coevolutionary approach to function optimization. In The third parallel problem solving from nature (pp. 249–257). Berlin: Springer.

    Chapter  Google Scholar 

Download references

Acknowledgements

This work is supported by the Natural Science Foundation of Hebei Province of China No. E2010001026.

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Authors and Affiliations

  1. School of Water Conservancy and Hydropower, Hebei University of Engineering, Handan, 056021, China

    Liying Wang

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  1. Liying Wang
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Correspondence to Liying Wang.

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The author gratefully acknowledges the support of the Natural Science Foundation of Hebei Province of China.

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Wang, L. An improved cooperative particle swarm optimizer. Telecommun Syst 53, 147–154 (2013). https://doi.org/10.1007/s11235-013-9688-z

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