基于改进量子免疫克隆多目标优化算法的火力分配问题

计算机工程与科学

基于改进量子免疫克隆多目标优化算法的火力分配问题

冯超1，姚鹏2，景小宁2,李晓阳3

（1.66133部队，北京 100041;2.空军工程大学航空航天工程学院，陕西西安 710038;

3.陆军装甲兵学院蚌埠校区，安徽蚌埠 233050）

收稿日期:2015-11-20 修回日期:2016-05-12 出版日期:2017-12-25 发布日期:2017-12-25
基金资助:
航空科学基金（20155196022）

Weapon-target assignment based on improved quantum-inspired

immune clonal multi-objective optimization algorithm

FENG Chao1,YAO Peng2,JING Xiao-ning2,LI Xiao-yang3

（1.Troop 66133,Beijing 100041;

2.College of Aeronautics and Astronautics Engineering,Air Force Engineering University,Xi’an 710038;

3.Armored Force Institute of Army(Bengbu Campus),Bengbu 233050,China）

Received:2015-11-20 Revised:2016-05-12 Online:2017-12-25 Published:2017-12-25

摘要/Abstract

摘要：

针对传统火力分配中存在武器资源浪费的情况，以对敌目标与网络攻击收益最大、己方武器消耗最小为目标，建立一种考虑毁伤概率约束条件的多目标火力分配模型。对标准量子免疫克隆多目标优化算法进行优化，引入了混沌机制，修复不可行解，并对搜索策略和多样性保持策略进行改进，设计了一种改进的量子免疫克隆多目标优化算法。通过实验仿真，验证了模型的正确性与算法的优越性。相比于传统量子免疫克隆算法，改进算法的性能平均提高了23%。

关键词: 火力分配, 量子克隆免疫多目标算法, 毁伤概率约束

Abstract:

Aiming at the problem of waste of weapon resources in traditional weapon target assignment (WTA), and to realize the maximum benefit of target and network attacks and the minimum consumption of weapons, we establish a multi-objective WTA model, which takes the damage probability constraint into consideration. We improve the standard quantum immune clonal multi-objective optimization algorithm (QICMOA), introduce the chaos mechanism, and repair the infeasible solutions. We also make improvement for the search strategy and diversity maintaining strategy and design an improved QICMOA. Simulation experiments verify the correctness of the model and the superiority of the algorithm. Compared with the traditional QICMOA, the performance of the new algorithm is improved by 23% on average.

Key words:

weapon-target assignment(WTA), quantum immune clonal multi-objective optimization algorithm(QICMOA), damage probability constraint

冯超1，姚鹏2，景小宁2,李晓阳3. 基于改进量子免疫克隆多目标优化算法的火力分配问题[J]. 计算机工程与科学.

FENG Chao1,YAO Peng2,JING Xiao-ning2,LI Xiao-yang3.

Weapon-target assignment based on improved quantum-inspired

immune clonal multi-objective optimization algorithm

[J]. Computer Engineering & Science.