Abstract: To improve the low accuracy and premature convergent in traditional Particle Swarm Optimization（PSO） algorithm, the chaos cloud particle swarm algorithm based on golden section evaluation criteria（CCGPSO） is proposed in this paper. This method divides the particle swarm into standard particle, chaos cloud particle and cloud particle using the golden section judge principles according to fitness level, every sub swarm particle has respective different algorithm operations. The golden section enables particle swarm to search the entire solution space, solves the problems of easily falling into local optimum and low accuracy in basic PSO. This paper chooses four reference functions to have a test and compared with chaos cloud particle swarm optimization （CCPSO）. The simulation results demonstrate CCGPSO has high optimization precision and convergence speed.

Key words: golden section, Particle Swarm Optimization（PSO）； chaos optimization, X condition cloud generator

摘要： 针对传统粒子群算法寻优精度不高、易早熟的缺点，提出了基于黄金分割评判准则的混沌云粒子群（CCGPSO）算法。该算法利用黄金分割评判准则，将粒子群按照适应度大小分为标准粒子、混沌云粒子、云粒子三个子群，分别进行不同的算法操作。黄金分割的引入使整个粒子群可以搜索到全部解空间，解决了标准粒子群算法易陷入局部最优解和寻优精度不高的问题。选取了四种典型函数测试，并与混沌云粒子群算法（CCPSO）比较。仿真结果表明CCGPSO具有较高的寻优精度和收敛速度。

关键词: 黄金分割, 粒子群算法, 混沌算法, X条件云发生器