Chaotic hopfield neural network swarm optimization and its application

Yanxia Sun, Zenghui Wang, Barend Jacobus Van Wyk

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Article number873670
JournalJournal of Applied Mathematics
Publication statusPublished - 2013
Externally publishedYes

ASJC Scopus subject areas

  • Applied Mathematics


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