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. First ,rate of cluster focus distance changing was introduced in this new algorithm and the weight was formulated as a function of this factor which provides the algorithm with effective dynamic adaptability. Secondly, a method of effective judgment of early stagnation is embedded in the algorithm. Once the early maturity is retrieved, the algorithm mutates particles to jump out of the local optimum particle according to the structure mutation so as to reduce invalid iteration. Experiments on high-dimension test functions indicate that the improved algorithm is superior to classical PSO algorithm and quantum-behaved PSO algorithm.