基于智能对抗进化的联合火力打击任务规划方法

doi:10.3969/j.issn.1000-1093.2019.06.020

兵工学报 ›› 2019, Vol. 40 ›› Issue (6): 1287-1296.doi: 10.3969/j.issn.1000-1093.2019.06.020

基于智能对抗进化的联合火力打击任务规划方法

刘昊¹，张策¹，丁文韬²

(1.国防大学联合作战学院, 河北石家庄 050000; 2.国防大学研究生院, 北京 100091)

收稿日期:2018-09-06 修回日期:2018-09-06 上线日期:2019-08-14
通讯作者: 张策（1965—），男，教授，博士生导师 E-mail:wentaoding2131@gfdx.edu.cn
作者简介:刘昊（1983—），男，博士研究生。 E-mail： 176892033@qq.com
基金资助:
国家社会科学基金项目（16GJ003-051）

Joint Fire Attack Mission Planning Method Based on Intelligent Confrontation Evolution

LIU Hao¹，ZHANG Ce¹，DING Wentao²

(1.Joint Operation College, National Defense University, Shijiazhuang 050000, Hebei， China;2.Graduate School, National Defense University, Beijing 100091, China)

Received:2018-09-06 Revised:2018-09-06 Online:2019-08-14

摘要/Abstract

摘要： 针对常规联合火力打击任务规划方法很少涉及敌我对抗，导致评估环境发生变化的问题，提出一种基于敌我对抗进化的智能对抗进化算法。该算法以遗传算法为基础，将模拟生物竞争机制引入敌我双种群，互为评估条件实施对抗进化。依据敌我战场态势图构建观察-判断-决策-打击（OODA）超网络，计算OODA循环效率、确定敌我打击排序,通过多代对抗进化获得能够适应战场动态变化的任务规划最优个体。仿真结果表明：多代进化后的最优个体相比于标准优化结果，战场动态适应性更强，联合火力打击胜率更高，应对突发情况的响应机制更完善，能够有效地解决联合火力打击任务规划的评估优化问题。

关键词: 联合火力打击, 任务规划, 智能对抗进化, 遗传算法, 超网络, 观察-判断-决策-打击循环, 人工智能

Abstract: In view of the fact that the conventional joint fire attack mission planning method rarely involves an issue of friend-foe confrontation leading to the change in evaluation environment, a smart confrontation evolution algorithm based on the evolution of friend-foe confrontation is proposed. The proposed algorithm is based on genetic algorithm, in which the simulation of biological competition mechanism is introduced into the two populations of friend and foe for implementing the confrontational evolution. An observe-orient-decide-act (OODA) super-network is constructed based on the battlefield situation map, and then the OODA cycle efficiency is calculated to determine the order of friend and foe attacks. The task-planning optimal individuals can adapt to the dynamic changes of the battlefield through the confrontation evolution of multiple generations. The simulated results show that the multi-generation evolutionary optimal individual has stronger dynamic adaptability, and the joint firepower strike rate is higher. The response mechanism to respond to the emergencies is more perfect, which can effectively solve the evaluation optimization issues of joint firepower mission planning. Key

Key words: jointfireattack, missionplanning, intelligentconfrontationevolution, geneticalgorithm, super-network, observe-orient-decide-actcycle, artificialintelligence

中图分类号:

E837

刘昊，张策，丁文韬. 基于智能对抗进化的联合火力打击任务规划方法[J]. 兵工学报, 2019, 40(6): 1287-1296.

LIU Hao，ZHANG Ce，DING Wentao. Joint Fire Attack Mission Planning Method Based on Intelligent Confrontation Evolution[J]. Acta Armamentarii, 2019, 40(6): 1287-1296.

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基于智能对抗进化的联合火力打击任务规划方法

Joint Fire Attack Mission Planning Method Based on Intelligent Confrontation Evolution

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