基于改进教-学算法的无人机航路规划

发布时间：2018-05-03 17:55

本文选题：教-学优化算法 + 无人机　；参考：《计算机应用》2016年09期

【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。
[Abstract]:In order to solve the problem of slow convergence and easy to fall into local optimum in the course of route planning by traditional teaching-learning optimization (TLBO) algorithm, an adaptive cross-teaching-learning optimization algorithm (AC-TLBOA) is proposed. Firstly, the teaching factor of the traditional TLBO algorithm changes with the number of iterations, and the learning speed of the algorithm is improved. Secondly, when the algorithm is likely to fall into a local optimum, a certain disturbance is added. Finally, in order to further improve the convergence effect of the algorithm, genetic algorithm crossover is introduced into the algorithm. UAV route planning is based on traditional teacher-learning optimization algorithm, adaptive cross-teaching-learning optimization algorithm AC-TLBOA and quantum particle swarm optimization (QPSO) algorithm. The simulation results show that, in 10 times of planning, The adaptive cross-teaching-learning optimization (AC-TLBOO) algorithm has found the global optimal path eight times, while the traditional teaching-learning optimization algorithm and the quantum particle swarm optimization (QPSO) algorithm have found only two times and one times respectively. Moreover, the convergence speed of the adaptive cross-teaching-learning optimization algorithm AC-TLBOA is higher than that of the other two algorithms.
【作者单位】：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;
【基金】：国家自然科学基金资助项目(61273075)~~
【分类号】：V279;TP18

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