Abstract: A novel algorithm is presented on the base of quantum behaved particle swarm optimization，which is aimed at resolving the problem of slow convergence rate in optimizing higher dimensional sophisticated functions and being trapped into local minima easily.Chaos algorithm is incorporated to traverse the whole solution space；besides a new strategy of self adapting adjustment of inertia weight according to the current particles’ fitness is combined also to balance the capability of local search and global search.The experimental results of some typical trial functions show that the proposed algorithm not only has great advantage of fast convergence rate and computational precision in solution，but also can avoid the premature effectively with a better performance than Standard Particle Swarm Optimization（SPSO） and Quantum behaved Particle Swarm Optimization（QPSO）.

Key words: swarm intelligence, particle swarm optimization, quantum-behaved particle swarm optimization, self-adapting adjustment of inertia weight

摘要： 针对量子粒子群优化算法在处理高维复杂函数收敛速度慢、易陷入局优的问题，利用混沌算子的遍历性提出了基于惯性权重自适应调整的混沌量子粒子群优化算法。该算法在运行过程中根据粒子适应值的优劣情况，相应采取不同的惯性权重策略，以调节粒子的全局搜索和局部搜索能力。对几个典型函数的测试结果表明，该算法在收敛速度和精度上有大幅度的提高，且有很强的避免陷入局优的能力，性能远远优于一般的粒子群算法和量子粒子群算法。

关键词: 群体智能, 粒子群优化, 量子粒子群优化, 惯性权重自适应调整