[1]刘振,李伟,任建存.多基地多UCAV任务分配建模及求解方法[J].东南大学学报(自然科学版),2019,49(1):88-93.[doi:10.3969/j.issn.1001-0505.2019.01.013]
 Liu Zhen,Li Wei,Ren Jiancun.Modeling of multi-base multi-UCAV task allocation and its solving method[J].Journal of Southeast University (Natural Science Edition),2019,49(1):88-93.[doi:10.3969/j.issn.1001-0505.2019.01.013]
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多基地多UCAV任务分配建模及求解方法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
49
期数:
2019年第1期
页码:
88-93
栏目:
自动化
出版日期:
2019-01-20

文章信息/Info

Title:
Modeling of multi-base multi-UCAV task allocation and its solving method
作者:
刘振李伟任建存
海军航空大学岸防兵学院, 烟台 264001
Author(s):
Liu Zhen Li Wei Ren Jiancun
College of Coastal Defense Force, Naval Aeronautical University, Yantai264001, China
关键词:
任务分配 量子遗传算法 分布估计 合同网
Keywords:
task allocation quantum genetic algorithm estimation of distribution contract net
分类号:
TP15;E955
DOI:
10.3969/j.issn.1001-0505.2019.01.013
摘要:
为有效求解多基地情形下的无人作战飞机(UCAV)任务分配问题,在考虑任务收益、任务负载以及时间因素的条件下,建立了多基地多无人作战飞机的任务分配模型,并提出采用初始分配和动态分配相结合的求解方法.为提高初始任务分配问题的求解效率,将量子遗传算法融入了扩展紧致遗传算法的边缘积模块思想中,提出一种分布估计量子遗传算法(ED-QGA),用于初始全局最优分配,当出现突发动态任务时,采用合同网进行分配方案的局部调整.最后对提出模型和算法进行了仿真分析.结果表明,相比基于种群的增量学习算法和多粒度的量子遗传算法,分布估计量子遗传算法求解获得效能值分别提高了33.4%和7.2%,与基本合同网和扩展合同网相比,效能值分别提高了9.2%和5%,因此能够有效提高UCAV整体作战效能.
Abstract:
Aiming at the problem of task allocation under condition of multi-base and multi-UCAV, a model was established considering factors of task reward, task load and time, a solving method for the problem of initial allocation combined with dynamic allocation was proposed. To improve the efficiency of initial task allocation, the marginal product model of extended compact genetic algorithm could be brought into the quantum genetic algorithm, a novel quantum genetic algorithm inspired by estimation of distribution algorithm(ED-QGA)was proposed to obtain the comprehensive optimum results. The contract net was used to adjust the task allocation project when the pop-up threat appeared. Finally, simulations were used to verify the performance of the proposed model and the algorithm. Experimental results show that the effectivenesses are improved by 33.4% and 7.2% by ED-QGA, compared with population-based incremental learning(PBIL)algorithm and multi-granularity quantum genetic algorithm(MQGA), by 9.2% and 5% compared with contract net and extended contract net.

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备注/Memo

备注/Memo:
收稿日期: 2018-07-05.
作者简介: 刘振(1983—),男,博士,讲师,hylz1008@126.com.
基金项目: 国家自然科学基金资助项目(51605487).
引用本文: 刘振,李伟,任建存.多基地多UCAV任务分配建模及求解方法[J].东南大学学报(自然科学版),2019,49(1):88-93. DOI:10.3969/j.issn.1001-0505.2019.01.013.
更新日期/Last Update: 2019-01-20