A chaotic particle swarm optimization algorithm(HCPSO) is proposed based on the model of hierarchical multi-
subpopulation structure, which uses chaotic mutation in nonlinear and decreasing inertia weight. For reinforcing the local
searching ability, every particle has its own new global best position by using chaotic searching strategy. The new global best
position is the average position of several individuals which are picked out as exemplars when the new global best position
is updated in each dimension. The radius of the chaotic searching region is adaptively adjusted according to the distance
between the particle’s personal best position and the average position. The simulation results show that HCPSO is more
effective to keep a balance of global and local searching ability, and to overcome the slow convergence and prematurity.