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

摘要： 针对量子粒子群优化算法（quantum delta Particle Swarm Optimization，PSO）在处理高维复杂函数时存在收敛速度慢、易陷入局部最优和算法通用性不强等缺点，提出了一种基于混沌优化机制的双量子粒子群优化算法。它借鉴群体位置方差的早熟判断机制，同时提出了一种逐步缩小搜索变量空间的新方法。典型数值实验表明，该算法效率高、优化性能好、对初始位置具有很强的鲁棒性。尤其是该算法具有很强的避免局部极小能力，其性能远远优于单一优化方法。

关键词: 双量子粒子群优化算法, 双混沌优化机制, 早熟机制