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