一种基于启发式优化的网状多任务筹划方法研究

doi:10.12305/j.issn.1001-506X.2026.04.22

系统工程与电子技术 ›› 2026, Vol. 48 ›› Issue (4): 1340-1348.doi: 10.12305/j.issn.1001-506X.2026.04.22

• 系统工程 • 上一篇

一种基于启发式优化的网状多任务筹划方法研究

李尧尧¹(), 李人杰²^,*(), 孙长军¹, 赵晓峰², 董平¹

1. 北京跟踪与通信技术研究所，北京 100094
2. 北京电子工程总体研究所，北京 100854

收稿日期:2024-12-16 修回日期:2025-04-03 出版日期:2025-06-11 发布日期:2025-06-11
通讯作者: 李人杰 E-mail:liyaoyaobit@163.com;lirenjie_hk@163.com
作者简介:李尧尧（1982—），男，助理研究员，博士，主要研究方向为系统工程、任务筹划分析
孙长军（1983—），男，副研究员，硕士，主要研究方向为系统工程、系统任务筹划
赵晓峰（1983—），男，高级工程师，硕士，主要研究方向为指挥控制策略、大型任务筹划模型设计
董　平（1982—），男，副研究员，硕士，主要研究方向为指挥控制策略与任务设计、筹划模型构建

Research on a networked multi-task planning method based on heuristic optimization

Yaoyao LI¹(), Renjie LI²^,*(), Changjun SUN¹, Xiaofeng ZHAO², Ping DONG¹

1. Beijing Institute of Tracking and Telecommunication Technology，Beijing 100094，China
2. Beijing Institute of Electronic System Engineering，Beijing 100854，China

Received:2024-12-16 Revised:2025-04-03 Online:2025-06-11 Published:2025-06-11
Contact: Renjie LI E-mail:liyaoyaobit@163.com;lirenjie_hk@163.com

摘要/Abstract

摘要：

针对多类复杂信息任务中多条任务独立筹划难以实现全局效能最优的问题，提出一种基于启发式优化的多任务筹划方法，建立任务网络优化目标与约束条件模型，构建面向全局的启发式优化算法流程。构建常规任务场景和大规模任务场景，使用传统的单任务筹划方法和所提多任务筹划方法进行仿真试验。结果表明，所提方法在98%以上的情况优于传统任务链或与之相当，场景规模越大优势越显著，且计算所需时间始终处于同一数量级。所提全局多任务筹划方法在筹划效能、时间、灵活度等方面的潜在优越性，在复杂任务领域有重要的应用前景。

关键词: 任务筹划, 启发式优化, 任务网络, 大规模场景

Abstract:

This article proposes a multi task planning method based on heuristic optimization to address the problem of difficulty in achieving global efficiency optimization through independent planning of multiple tasks in complex information tasks. A task network optimization objective and constraint model are established, and a global oriented heuristic optimization algorithm flow is constructed. Both conventional task scenarios and large-scale task scenarios are constructed, and simulation experiments are conducted using traditional single task planning methods and the multi task planning method proposed in this paper. The results show that the proposed method outperforms or is comparable to traditional task chains in over 98% of cases, with the advantage becoming more significant as the scenario size increases, and the required computation time remains at the same order of magnitude. The proposed global multitasking planning methods has advantages in planning efficiency, time, flexibility, etc., and has important application prospects in the field of complex tasks.

Key words: task planning, heuristic optimization, mission network, large-scale scenario

中图分类号:

TP 18

李尧尧, 李人杰, 孙长军, 赵晓峰, 董平. 一种基于启发式优化的网状多任务筹划方法研究[J]. 系统工程与电子技术, 2026, 48(4): 1340-1348.

Yaoyao LI, Renjie LI, Changjun SUN, Xiaofeng ZHAO, Ping DONG. Research on a networked multi-task planning method based on heuristic optimization[J]. Systems Engineering and Electronics, 2026, 48(4): 1340-1348.

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序号	信息源	通信链路	用户	构网方式优化目标	构链方式优化目标	提升比例/%
1	3	3	8	645.93	557.29	15.91
2	3	3	8	724.96	637.1	13.79
3	3	3	20	831.87	811.82	2.47
4	3	3	20	7 265.77	7 139.2	1.77
5	3	8	20	864.91	852.88	1.41

序号	资源	用户	构网方式 $ \mathit{f}\left({\mathit{X}}_{\mathit{w}}\right) $	构链方式$ \mathit{f}\left({\mathit{X}}_{\mathit{l}}\right) $	提升比例/%
1	100	200	11 286	9 431.89	19.66
2	200	100	22 510.62	1 8815.2	19.64
3	200	150	22 553.19	18 850.44	19.64
4	100	150	11 281.05	9 432.71	19.60
5	200	50	22 468.05	18 787.02	19.59

链序号	信息源序号	通信链路序号	用户序号
1	1	1	1
2	3	2	2
3	3	1	3
4	2	2	4
5	1	2	5
6	3	1	6
7	2	1	7
8	1	2	8

链序号	信息源序号	通信链路序号	用户序号
1	1	1	1
2	3	2	3
3	3	1	4
4	2	2	5
5	1	2	6
6	3	1	7
7	2	1	8

序号	储备基地数量	运输机数量	目的地数目	提升比例/%
1	3	3	20	19.42
2	3	4	20	19.78
3	3	6	20	19.78
4	4	3	20	12.35
5	4	4	20	13.05
6	4	6	20	12.67
7	6	3	20	12.64
8	6	4	20	5.02
9	6	6	20	2.24

一种基于启发式优化的网状多任务筹划方法研究

Research on a networked multi-task planning method based on heuristic optimization

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