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Chaotic Hopfield Neural Network Swarm Optimization and Its Application

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Listed:
  • Yanxia Sun
  • Zenghui Wang
  • Barend Jacobus van Wyk

Abstract

A new neural network based optimization algorithm is proposed. The presented model is a discrete‐time, continuous‐state Hopfield neural network and the states of the model are updated synchronously. The proposed algorithm combines the advantages of traditional PSO, chaos and Hopfield neural networks: particles learn from their own experience and the experiences of surrounding particles, their search behavior is ergodic, and convergence of the swarm is guaranteed. The effectiveness of the proposed approach is demonstrated using simulations and typical optimization problems.

Suggested Citation

  • Yanxia Sun & Zenghui Wang & Barend Jacobus van Wyk, 2013. "Chaotic Hopfield Neural Network Swarm Optimization and Its Application," Journal of Applied Mathematics, John Wiley & Sons, vol. 2013(1).
    • Handle: RePEc:wly:jnljam:v:2013:y:2013:i:1:n:873670
    DOI: 10.1155/2013/873670
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    References listed on IDEAS

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    1. Liu, Bo & Wang, Ling & Jin, Yi-Hui & Tang, Fang & Huang, De-Xian, 2005. "Improved particle swarm optimization combined with chaos," Chaos, Solitons & Fractals, Elsevier, vol. 25(5), pages 1261-1271.
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