基于改进遗传算法的航材装载优化

目的 通过对现行航材装载问题进行分析,优化提高工作中航材的装载效率,为部队航材智能装载提供思路和方法.方法 确定初始可理想化条件和装载过程中需满足的约束条件,以航材的摆放顺序和旋转方式为基因值,利用装载方案所需装载箱数量和重心偏移度为适应度函数去评估解的优劣;采取精英选择策略去指导进化方向,子代的产生选择相对偏随机秘钥交叉及顺序变异,避免选择变异过程中可能产生基因值的冲突,并用最小二乘法对航材储运装载算法输入参数进行拟合,找到平衡时间和装载箱使用数量的最佳输入值.结果 通过与实际人工装载进行比对,将装载箱平均使用率从78.1％提升至84.45％,并完成了对航材的分类.结论 该方法具有优化航材装载方案的能力,能够提升装载工作效率,对部队航材智能装载发展具有重要意义.

Air Material Loading Optimization Based on Improved Genetic Algorithm

The work aims to optimize and improve the loading efficiency of air materials in the work through the analysis on the current air material loading problems, so as to provide ideas and methods for the intelligent loading of army air materials. The initial idealized conditions and the constraints required to be satisfied in the loading process were determined. With the placement order and rotation mode of air materials as genetic values, the number of loading boxes and the deviation of the center of gravity were used as fitness functions to evaluate the solutions. Elite selection strategy was adopted to guide the direction of evolution. The generation and selection of progeny were relatively partial random key crossover and sequence variation, which avoided the conflict of gene value possibly occurring in the process of selection variation. The least square method was used to fit the input parameters of the algorithm of storage, transportation and loading of air materials, and the optimal input values of the balance time and the number of containers used were found. By comparing with the actual manual loading, the average utilization rate of the loading box was increased from 78.1% to 84.45%, and the classification of air materials was completed. This method has the ability to optimize the loading scheme of air materials, can improve the loading efficiency, and is of great significance for the development of intelligent loading of army air materials.