基于多目标MSQPSO算法的UAVS协同任务分配

doi:10.3969/j.issn.0372-2112.2017.08.008

电子学报 ›› 2017, Vol. 45 ›› Issue (8): 1856-1863.DOI: 10.3969/j.issn.0372-2112.2017.08.008

所属专题：多目标优化

基于多目标MSQPSO算法的UAVS协同任务分配

韩博文, 姚佩阳, 孙昱

空军工程大学信息与导航学院, 陕西西安 710077

收稿日期:2016-09-26 修回日期:2017-01-20 出版日期:2017-08-25
- 作者简介:
- 韩博文,男.1992年12月出生,河南周口人.现为空军工程大学信息与导航学院硕士研究生,主要从事无人机系统指挥控制建模与决策方面的有关研究.E-mail:hwyyxlw@163.com;姚佩阳,男.1960年7月出生,陕西西安人,教授、博士生导师.1982年毕业于西北电讯工程学院.主要从事指挥控制理论与技术、决策理论与技术等方面的有关研究.E-mail:ypy_664@163.com;孙昱,男.1989年8月出生,江西吉安人.现为空军工程大学信息与导航学院博士研究生,主要从事指挥控制系统建模与仿真方面的有关研究.
- 基金资助:
- 国家自然科学基金 (No.61573017）

UAVS Cooperative Task Allocation Based on Multi-Objective MSQPSO Algorithm

HAN Bo-wen, YAO Pei-yang, SUN Yu

Information and Navigation College, Air Force Engineering University, Xi'an, Shaanxi 710077, China

Received:2016-09-26 Revised:2017-01-20 Online:2017-08-25 Published:2017-08-25
- Supported by:
- National Natural Science Foundation of China (No.61573017)

摘要/Abstract

摘要： 针对无人机系统协同作战过程中存在多任务类型时序约束以及单目标优化决策欠佳问题，提出了一种利用多策略融合量子粒子群算法进行多目标优化的解决方法.在建立任务分配模型过程中，考虑不同类型任务的时序约束和多无人机协同约束，并抽象出无人机执行不同类型任务的能力，使模型更加符合实际作战情况.利用佳点集构造理论、变尺度混沌因子、量子变异操作与动态惯性权重对量子粒子群算法（Quantum Particle Swarm Optimization，QPSO）进行改进.最后通过采取多目标优化决策来选取相应的分配方案，仿真结果验证了所提算法的有效性与优越性.

关键词: 无人机, 任务分配, Pareto多目标优化, 量子粒子群, 多策略融合

Abstract: Unmanned aerial vehicle system (UAVS) cooperative combat model with temporal constraint of task type is insufficient making decision by single objective optimization.The multi-objective multi-strategy fusion quantum particle swarm optimization (MSQPSO) algorithm was proposed.To establish the task allocation model more accord with the actual operation situation,adding temporal constraint of task type and multi-UAV cooperative constraint,and abstracting the various capabilities of UAV.The quantum particle swarm optimization was improved by good-point set theory,scale chaos factor,quantum mutation and dynamic inertia weight.The multi-objective optimization was adopted to make decision.The final simulation results verify the effectiveness and superiority of the proposed MSQPSO algorithm.

Key words: unmanned aerial vehicle, multi-task allocation, multi-objective optimization, quantum particle swarm, multi-strategy fusion

中图分类号:

TP301

韩博文, 姚佩阳, 孙昱. 基于多目标MSQPSO算法的UAVS协同任务分配[J]. 电子学报, 2017, 45(8): 1856-1863.

HAN Bo-wen, YAO Pei-yang, SUN Yu . UAVS Cooperative Task Allocation Based on Multi-Objective MSQPSO Algorithm[J]. Acta Electronica Sinica, 2017, 45(8): 1856-1863.

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基于多目标MSQPSO算法的UAVS协同任务分配

UAVS Cooperative Task Allocation Based on Multi-Objective MSQPSO Algorithm

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