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

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