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