关键词: 双量子粒子群优化算法, 双混沌优化机制, 早熟机制

Abstract: Using quantum delta Particle Swarm Optimization（PSO） to handle complex functions with high-dimension has the problems of low convergence speed and sensitivity to local convergence.This paper proposes a double quantum delta particle swarm optimization based on chaos optimization strategy.It adopts prematurity judge mechanism by the variance of the population’s fitness and a new method of reducing the searching space of variable optimized is proposed.Numerical simulation results on benchmark complex functions with high dimension show that the hybrid particle swarm optimization is effective，efficient，fairly robust to initial conditions.Especially the hybrid particle swarm optimization is of strong ability to avoid being trapped in local minima，and performances are fairly superior to single method.

Key words: double quantum delta particle swarm optimaziton, double chaos optimization, quickly convergence strategy