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论文:2016,Vol:34,Issue(1):41-46 |
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引用本文： |
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高颖, 陈旭, 周士军, 郭淑霞. 基于改进蚁群算法的多批次协同三维航迹规划[J]. 西北工业大学学报 |
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Gao Ying, Chen Xu, Zhou Shijun, Guo Shuxia. Planning Based on Improved Ant Colony Algorithm Multiple Batches Collaborative Three-Dimensional Track[J]. Northwestern polytechnical university |
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| 基于改进蚁群算法的多批次协同三维航迹规划 |
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| 高颖1,3, 陈旭1, 周士军2, 郭淑霞2 |
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1. 西北工业大学 航海学院, 陕西 西安 710072;
2. 西北工业大学 无人机特种技术国防重点实验室, 陕西 西安 710065;
3. 光电控制技术重点实验室, 河南 洛阳 471009 |
| 摘要: |
| 针对基本蚁群算法容易陷入局部寻优、收敛速度慢的缺陷以及解决多批次协同航迹规划问题的需要,提出了基于改进蚁群算法的多批次三维航迹规划算法。该算法采用基于加权排序的信息素更新规则,扩大各优劣蚂蚁的差异,提高了算法收敛速度,并采用了一种信息素挥发系数的随机自适应调节方法,在确保收敛速度的同时使算法具有全局寻优,解决了基本蚁群算法容易过早陷入局部最优缺点;在此基础上,引入蚂蚁子群间多约束条件下的协同进化策略,解决了多批次协同三维航迹规划。仿真结果表明:改进的蚁群算法在运算效率和收敛性上明显优于基本蚁群算法,多批次协同航迹规划能有效提高无人机的作战效能。 |
| 关键词:
加权排序
自适应调节
多批次协同
三维航迹规划
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| Planning Based on Improved Ant Colony Algorithm Multiple Batches Collaborative Three-Dimensional Track |
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| Gao Ying1,3, Chen Xu1, Zhou Shijun2, Guo Shuxia2 |
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1. College of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China;
2. Science and Technology on UAV Laboratory at Northwestern Polytechnical University, Xi'an 710065, China;
3. Science and Technology on Electro-Optic Control Laboratory, Luoyang 471009, China |
| Abstract: |
| For basic ant colony algorithm is easy to fall into local optimization, slow convergence and resolve defects more batches of cooperative route planning needs, proposed programming algorithm based on improved ant colony algorithm multiple batches three-dimensional track. The algorithm is based on the weighted sort pheromone update rules, expand differences merits of ants, improve the convergence speed, and uses a random pheromone evaporation coefficient of adaptive methods, ensuring at the same time make the algorithm convergence speed global optimization, to solve the basic ant colony algorithm is easy to fall into local prematurely most advantages and disadvantages; on this basis, introduced ants subgroup under more constrained conditions of co-evolution strategy to solve the three-dimensional track multiple batches collaborative planning. Simulation results show that: the improved ant colony algorithm in computational efficiency and convergence on the basic ant colony algorithm was superior, multi-batch cooperative path planning can improve the combat effectiveness of UAVs. |
| Key words:
ant colony optimization
computational efficiency
computer simulation
convergence of numerical methods
cost functions
data fusion
efficiency
flowcharting
global optimization
matrix algebra
motion planning
probability
unmanned aerial vehicles(UAV)
adaptive
cooperative path planning
digital mapping
many batches synergies
pheromone
3-D path planning
weighted sort
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| 收稿日期: 2015-09-14
修回日期:
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| DOI: |
| 基金项目: 光电控制技术重点实验室与航空科学基金(20145153027)联合资助 |
| 通讯作者:
Email： |
| 作者简介: 高颖(1965-),西北工业大学副教授,主要从事虚拟现实及数据融合研究。
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| 作者相关文章 |
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| 高颖 在本刊中的所有文章 |
| 陈旭 在本刊中的所有文章 |
| 周士军 在本刊中的所有文章 |
| 郭淑霞 在本刊中的所有文章 |
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