改进量子粒子群算法在航材初始配置中的应用

Application of improved quantum particle swarm algorithm in initial configuration of aircraft spare parts

针对量子粒子群优化算法在优化配置中存在过早收敛和全局寻优能力不完美的情况,从收缩扩张系数和进化因子方面进行改进。在航材配置模型构造方面,以改进系统保障率为目标函数的基础上引入成本变量,构建单位成本系统保障率最大的模型,选用波音首期设备清单为优化对象进行实例验证。实验证明,改进的量子粒子群算法求解结果更优,收敛速率更快。

Aiming at premature convergence and imperfect global optimization ability of quantum particle swarm optimal algorithm in optimal allocation, contraction-expansion coefficient and evolutionary factors are improved. From the aspect of aircraft spare parts allocation model building, unit cost model with maximum system guarantee rate is constructed, in which cost variables are introduced based on traditional goal of improving system assurance rate. Boeing＇s first stage equipment list is used as optimized object to validate the model, proving the better optimal result and faster convergence rate of the improved algorithm.