Abstract
Abstract:
A new particle swarm optimization (PSO) algorithm with
has a chaotic neural network structure, is proposed. The
structure is similar to the Hop¯eld neural network with
transient chaos, and has an improved ability to search for
globally optimal solution and does not su®er from problems
of premature convergence. The presented PSO model is
discrete-time discrete-state. The bifurcation diagram of a
particle shows that it converges to a stable fixed point from
a strange attractor, guaranteeing system convergence.