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为改善粒子群算法的全局搜索能力和计算精度,在标准粒子群算法和量子理论的基础上,将平均最好位置引入粒子的状态更新过程,提出了1种基于量子行为改进的粒子群算法及不同的收缩-扩张因子取值策略。采用该量子粒子群算法对建立的典型双轴涡扇发动机部件级非线性模型进行了求解,结果表明:分段线性递减收缩-扩张因子适用于复杂的航空发动机隐式模型,其收敛效率和精度都较高,具有一定的工程应用价值。
In order to improve the global search ability and accuracy of particle swarm optimization, based on the standard particle swarm optimization algorithm and quantum theory, the best average position is introduced into the particle state updating process. A particle swarm optimization algorithm based on improved quantum behavior is proposed. Different contraction - expansion factor value strategy. The quantum particle swarm optimization algorithm was used to solve the component-level nonlinear model of a typical biaxial turbofan engine. The results show that the piecewise linear decreasing shrinkage-expansion factor is suitable for the complicated implicit model of aero-engine. The convergence efficiency and precision Are higher, with a certain value of engineering applications.