基于混合遗传算法的海军航空兵场站物资配送车辆调度智能优化  

Intelligent optimization of vehicle scheduling for material distribution in naval aviation station based on hybrid genetic algorithm

作　　者：阎哲 汪民乐 汪江鹏 闫少强 吴丰轩 YAN Zhe;WANG Minle;WANG Jiangpeng;YAN Shaoqiang;WU Fengxuan(Basic Disciplinary Department,Rocket Force University of Engineering,Xi’an 710025,China)

机构地区：[1]火箭军工程大学基础部,陕西西安710025

出　　处：《系统工程与电子技术》2023年第12期3908-3914,共7页Systems Engineering and Electronics

摘　　要：为提升海军航空兵场站物资配送车辆调度效率,根据海军航空兵场站物资配送任务特点,建立了物资配送车辆调度优化模型,提出了混合遗传算法(hybrid genetic algorithm,HGA)对模型进行了求解。在HGA中引入了模拟退火(simulated annealing,SA)操作对经典遗传算法(genetic algorithm,GA)进行了改进:选择适合模型的编码方式和交叉算子;使用类似路径构造的方法构建初始种群;在遗传操作产生子种群之后,通过SA操作寻找子种群邻域中的潜在优秀个体,提升算法局部搜索能力。最后,通过与经典GA的对比实验,验证了所提算法的有效性和可靠性。In order to improve the scheduling efficiency of material distribution vehicles in naval aviation stations,an optimization model for material distribution vehicle scheduling is established based on the characteristics of material distribution tasks in naval aviation stations,and a hybrid genetic algorithm(HGA)is proposed to solve the model.The HGA introduces the simulated annealing(SA)algorithm operation to improve the classical genetic algorithm(GA):choosing the coding method and crossover operator suitable for the model;using a method similar to path construction to construct the initial population;after the genetic operation generating the sub-population,the SA operation is used to find the potential outstanding individuals in the sub-population neighbourhood to improve the local search ability of the algorithm.Finally,the effectiveness and reliability of the proposed algorithm are verified by comparing the experiments with the classical GA.

关 键 词：混合遗传算法 海军航空兵场站 物资配送 智能优化 

分 类 号：E917[军事]