| 引用本文: | 颜骥,李相民,刘波.应用离散粒子群-郭涛算法分配多无人机协同任务.[J].国防科技大学学报,2015,37(4):165-171.[点击复制] |
| YAN Ji,LI Xiangmin,LIU Bo.Cooperative task allocation of multi-UAVs with mixed DPSO-GT algorithm[J].Journal of National University of Defense Technology,2015,37(4):165-171[点击复制] |
|
| |
|
|
| 本文已被：浏览 8499次 下载 6011次 |
| 应用离散粒子群-郭涛算法分配多无人机协同任务 |
| 颜骥1, 李相民1,2, 刘波3 |
|
(1.海军航空工程学院 兵器科学与技术系， 山东 烟台 264001;2.
2.中航工业洛阳电光设备研究所 光电控制技术重点实验室， 河南 洛阳 471023;3.2.中航工业洛阳电光设备研究所 光电控制技术重点实验室， 河南 洛阳 471023)
|
| 摘要: |
| 针对以往考虑时间窗约束的多无人机协同任务分配问题模型不能反映在有效时间窗内，任务执行时间对任务收益的影响及求解算法效率较低的问题。建立了将任务收益和任务执行时间直接联系起来的任务分配模型和可行解到粒子整数编码方式的映射，设计了混合离散粒子群-郭涛算法的组合优化问题求解策略。借助粒子群算法利用粒子自身信息和种群有用信息指导种群进化的本质特点，优化郭涛算法的适应性序列倒置操作；设计了可变的学习选择概率来选择个体的学习粒子，改进了序列倒置算子。仿真实验验证了该方法处理复杂任务分配问题的有效性。 |
| 关键词: 离散粒子群算法 郭涛算法 任务分配 有效时间窗 多无人机 |
| DOI：10.11887/j.cn.201504027 |
| 投稿日期：2014-10-27 |
| 基金项目:航空科学基金资助项目（20135184008） |
|
| Cooperative task allocation of multi-UAVs with mixed DPSO-GT algorithm |
| YAN Ji1, LI Xiangmin1,2, LIU Bo3 |
|
(1.Department of Ordnance Science and Technology, Naval Aeronautical and Astronautical University, Yantai 264001, China;2.
2. Science and Technology on Electro-optic Control Laboratory， Luoyang Institute of Electrooptical Equipment of AVIC, Luoyang 471023, China;3.2. Science and Technology on Electro-optic Control Laboratory， Luoyang Institute of Electrooptical Equipment of AVIC, Luoyang 471023, China)
|
| Abstract: |
| A general mathematics model for cooperative task allocation of multi-UAVs with time windows constrains was proposed which incorporating task gains and execution time directly, and simplifing the model formulation and algorithm designing. By defining a suitable particle structure, an algorithm based on the principles of discrete particle swarm optimization and Guo Tao algorithm was designed. The Inver-over Operator was directed by the swarm, the local and global optimal. Variable learning selection probability is introduced into the algorithm to select the learning particles, and the Inver-Over operator was modified. Simulation verifies the proposed task planning methodology for complex missions. |
| Keywords: discrete particle swarm optimization algorithm Guo Tao algorithm task allocation time windows of validity multi-UAVs |
|
|
