Abstract
A single particle structure of particle swarm optimization was analyzed which is found to have
some properties of a Chaos-Hopfield neural net work. A new model of particle swarm optimization
is presented. The model is a deterministic Chaos-Hopfield neural network swarm which is
different from the existing one with stochastic parameters. Its search orbits show an evolution
process of inverse period bifurcation from chaos to periodic orbits then to sink. In this evolution
process, the initial chaos-like search expands the optimal scope, and inverse period bifurcation
determines the stability and convergence of the search. Moreover, the convergence is
theoretically analyzed. Finally, the numerical simulation shows the basic procedure of the
proposed model and verifies its efficiency.