基于改进灰狼算法的舰载机弹药保障调度优化

doi:10.12305/j.issn.1001-506X.2024.04.15

系统工程与电子技术 ›› 2024, Vol. 46 ›› Issue (4): 1264-1272.doi: 10.12305/j.issn.1001-506X.2024.04.15

基于改进灰狼算法的舰载机弹药保障调度优化

刘哲¹^,²^,³, 马俊飞⁴, 陈佳峰⁴, 马嵩华¹^,²^,³^,*

1. 山东大学机械工程学院, 山东济南 250061
2. 高效洁净机械制造教育部重点实验室, 山东济南 250061
3. 山东大学机械工程国家级实验教学示范中心, 山东济南 250061
4. 湖南云箭集团有限公司, 湖南长沙 419503

收稿日期:2023-04-20 出版日期:2024-03-25 发布日期:2024-03-25
通讯作者: 马嵩华
作者简介:刘哲(1999—), 男, 硕士研究生, 主要研究方向为系统建模与可视化、调度优化求解
马俊飞(1982—), 男, 高级工程师, 博士, 主要研究方向为航空弹药总体设计
陈佳峰(1988—), 男, 高级工程师, 主要研究方向为航空弹药系统总体研究
马嵩华(1985—), 女, 副教授, 博士, 主要研究方向为知识工程、现代理论设计与方法
基金资助:
国家自然科学基金(51505254);国家自然科学基金(51975326);中国博士后基金(2021M691704);山东省博士后创新基金(202101017)

Carrier-based aircraft ammunition support scheduling optimization based on improved grey wolf optimizer algorithm

Zhe LIU¹^,²^,³, Junfei MA⁴, Jiafeng CHEN⁴, Songhua MA¹^,²^,³^,*

1. School of Mechanical Engineering, Shandong University, Jinan 250061, China
2. Key Laboratory of High-efficiency and Clean Mechanical Manufacture, Ministry of Education, Jinan 250061, China
3. Shandong University National Experimental Teaching Demonstration Center of Mechanical Engineering, Jinan 250061, China
4. Hunan Vanguard Group Company Limited, Changsha 419503, China

Received:2023-04-20 Online:2024-03-25 Published:2024-03-25
Contact: Songhua MA

摘要/Abstract

摘要：

针对航空母舰飞行甲板上舰载机弹药保障面临的调度效率不高的问题, 提出了一种改进灰狼优化(grey wolf optimizer, GWO)算法。根据甲板上多升降机多运输车的场景特点, 建立了由多车场出发、向多目标转运的问题模型。融合遗传算法算子交叉思想实现了对灰狼种群初始解的初步优化, 并通过直线转运路径中间点定义、整数编码、负整数标志分组等方法实现了对GWO算法求解过程的改进。同时, 增加了灰狼个体自由狩猎流程, 有效克服了结果陷入局部最优和早熟的问题。最终, 通过对场景实例的优化求解, 验证了所提方法的有效性和可行性。

关键词: 灰狼优化算法, 多车场, 多目标, 整数编码, 标志分组

Abstract:

An improved grey wolf optimizer (GWO) algorithm is proposed to solve the problem of inefficient scheduling faced by carrier-based aircraft ammunition support on the flight deck of aircraft carriers. According to the characteristics of the scenario of multiple lifts and multiple transport vehicles on the deck, the problem of transferring from multiple vehicle fields to multiple targets is modeled. The initial optimization of the initial solution of the grey wolf population is achieved by integrating the idea of genetic algorithm operator crossover, and the improvement of the solution process of the GWO algorithm is achieved by linear path transportation midpoint definition, integer encoding and negative integer sign grouping, etc. At the same time, the free hunting process of individual grey wolf is also added to effectively overcome the problem of results falling into local optimum and prematureness. Finally, the effectiveness and feasibility of the proposed method are verified through the optimal solution of the scenario example.

Key words: grey wolf optimizer (GWO) algorithm, multiple vehicle fields, multiple objectives, integer encoding, sign grouping

中图分类号:

刘哲, 马俊飞, 陈佳峰, 马嵩华. 基于改进灰狼算法的舰载机弹药保障调度优化[J]. 系统工程与电子技术, 2024, 46(4): 1264-1272.

Zhe LIU, Junfei MA, Jiafeng CHEN, Songhua MA. Carrier-based aircraft ammunition support scheduling optimization based on improved grey wolf optimizer algorithm[J]. Systems Engineering and Electronics, 2024, 46(4): 1264-1272.

图/表 15

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参数变量	含义
P	升降机数量
N	运输车数量
Q_n	运输车装载容量
K	目标工作点数量
k	运输暂存目标工作点数量
q_i	目标工作点i所需要的容量
v_m	运输车最大运输速度
[st_i, ft_i]	时间窗要求的目标工作点i的开始、结束时间
U	影响运输过程的相关因素集合
$i, j(\in K)=\left\{\begin{array}{l}(1, 2, \cdots, K), \text { 各目标工作点} \\ 0, \text { 货物升降机}\end{array}\right.$	物体标号
n(∈N)	各运输车
(x_i, y_i)	目标工作点i的位置坐标
$L_{i j}=\sqrt{\left(x_i-y_i\right)^2+\left(y_i-y_j\right)^2}$	目标工作点i和j之间的直线距离
$\theta_{i j}=\left\{\begin{array}{l}1, \text { 目标工作点从} i \text { 到} j \text { 满足顺序要求} \\ 0, \text { 目标工作点从} i \text { 到} j \text { 不满足顺序要求}\end{array}\right.$	顺序要求参数
$\alpha_{i j n}=\left\{\begin{array}{l}1, \text { 运输车} n \text { 经目标工作点} i \text { 到达} j \\ 0, \text { 运输车} n \text { 未经目标工作点} i \text { 到达} j\end{array}\right.$	路径参数
$\beta_{i n}=\left\{\begin{array}{l}1, \text { 运输车} n \text { 经过目标工作点} i \\ 0, \text { 运输车} n \text { 未经过目标工作点} i\end{array}\right.$	到达参数
$t_{i n}=\left\{\begin{array}{l}\text { 运输车} n \text { 到达目标点} i \text { 的时间, } \beta_{i n}=1 \\ 0, \beta_{i n}=0\end{array}\right.$	等待时间
t_m	第m组的总体等待时间

序号	X相对坐标	Y相对坐标	类型
01	-63	319	升降机
02	7	140	升降机
03	7	-206	升降机
1	-77	113	运输点
2	-95	77	运输点
3	-91	-30	运输点
4	-87	-83	运输点
5	0	243	运输点
6	-47	369	运输点
7	-78	370	运输点
8	-40	-351	运输点
9	-45	94	装载点
10	-55	52	装载点
11	-64	-42	装载点
12	-70	-95	装载点
13	28	243	装载点
14	-56	438	装载点
15	-85	406	装载点
16	-30	-368	装载点

算法	实验次数	最优解次数	最优解频率/%	平均收敛代数
改进GWO算法	20	17	85	85.0
遗传算法	20	11	55	112.5

序号	X相对坐标	Y相对坐标	类型
01	-63	319	升降机
02	7	140	升降机
03	7	-206	升降机
1	-77	113	运输点
2	-95	77	运输点
3	-91	-30	运输点
4	-87	-83	运输点
5	0	243	运输点
6	-47	369	运输点
7	-78	370	运输点
8	-40	-351	运输点
9	-202	245	运输点
10	-173	183	运输点
11	-122	109	运输点
12	-109	-591	运输点
13	-45	94	装载点
14	-55	52	装载点
15	-64	-42	装载点
16	-70	-95	装载点
17	28	243	装载点
18	-56	438	装载点
19	-85	406	装载点
20	-30	-368	装载点
21	-161	229	装载点
22	-136	183	装载点
23	-81	160	装载点
24	-75	-402	装载点

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基于改进灰狼算法的舰载机弹药保障调度优化

Carrier-based aircraft ammunition support scheduling optimization based on improved grey wolf optimizer algorithm

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