计算机应用 ›› 2016, Vol. 36 ›› Issue (9): 2626-2630.DOI: 10.11772/j.issn.1001-9081.2016.09.2626

• 行业与领域应用 • 上一篇    下一篇

基于改进教学算法的无人机航路规划

武巍, 邹杰   

  1. 中国航空工业集团公司洛阳电光设备研究所 光电控制技术重点实验室, 河南 洛阳 471000
  • 收稿日期:2016-02-24 修回日期:2016-03-25 出版日期:2016-09-10 发布日期:2016-09-08
  • 通讯作者: 武巍
  • 作者简介:武巍(1991-),男,河南洛阳人,硕士研究生,主要研究方向:航空火力控制;邹杰(1977-),男,河南郑州人,高级工程师,主要研究方向:火控系统工程、智能控制。
  • 基金资助:
    国家自然科学基金资助项目(61273075)。

Route planning method for unmanned aerial vehicle based on improved teaching-learning algorithm

WU Wei, ZOU Jie   

  1. Science and Technology on Electro-optic Control Laboratory, AVIC Luoyang Institute of Electro-Optical Equipment, Luoyang Henan 471000, China
  • Received:2016-02-24 Revised:2016-03-25 Online:2016-09-10 Published:2016-09-08
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61273075).

摘要: 针对传统教-学优化(TLBO)算法进行航路规划时收敛速度慢、容易陷入局部最优的问题,提出一种自适应交叉教-学优化(AC-TLBO)算法。首先,该算法令传统教-学优化(TLBO)算法的教学因子随着迭代次数而发生变化,提高算法的学习速度;其次,当算法可能要陷入局部最优时,加入一定的扰动,使算法尽可能地跳出局部最优;最后,为了进一步提升算法的收敛效果,在算法中引入遗传算法的交叉环节。利用传统教-学优化(TLBO)算法、自适应交叉教-学优化(AC-TLBO)算法和量子粒子群优化(QPSO)算法进行无人机航路规划,仿真结果表明,在10次规划中,自适应交叉教-学优化(AC-TLBO)算法有8次找到了全局最优路径,而传统教-学优化(TLBO)算法和量子粒子群优化(QPSO)算法分别只找到了2次和1次;而且自适应交叉教-学优化(AC-TLBO)算法的收敛速度高于另外两种算法。

关键词: 教-学优化算法, 无人机, 航路规划, 自适应交叉, 局部最优, 量子粒子群优化算法

Abstract: Aiming at the problem of slow convergence and being easy to fall into local optimum in the route planning of the traditional teaching-learning-based optimization algorithm, an adaptive crossover teaching-learning-based optimization algorithm was proposed. Firstly, the teaching factor of the algorithm was changed with the number of iterations, so the learning speed of the algorithm was improved. Secondly, when the algorithm was likely to fall into local optimum, a certain disturbance was added to make the algorithm jump out of local optimum as far as possible. Finally, in order to improve the convergence effect, the crossover link of genetic algorithm was introduced into the algorithm. Then the path planning of Unmanned Aerial Vehicle (UAV) was carried out by using the traditional teaching-learning-based optimization algorithm, the adaptive crossover teaching-learning-based optimization algorithm and the Quantum Particle Swarms Optimization (QPSO) algorithm. The simulation results show that in 10 times of planning, the adaptive crossover teaching-learning-based optimization algorithm finds the global optimal route for 8 times, while the traditional teaching-learning-based optimization algorithm and the QPSO algorithm find the route for only 2 times and 1 time respectively, and the convergence of the adaptive crossover teaching-learning-based optimization algorithm is faster than the other two algorithms.

Key words: teaching-learning-based optimization algorithm, Unmanned Aerial Vehicle (UAV), route planning, adaptive crossover, local optimum, Quantum Particle Swarms Optimization (QPSO) algorithm

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