基于组合优化算法的无人机航迹规划方法研究

A Study of UAV Trajectory Planning Approach Based on Combined Optimization Method

根据遗传算法与蚁群算法各自的特点,将两者进行有机结合构成GA-ACO(genetic algorithm-ant colony optimization)组合优化算法,并将其应用在航迹规划路径寻优中以获取高质量的飞行航路。首先采用全局搜索能力强的遗传算法进行全局快速搜索,选取遗传算法得到的较优解集合,构成蚁群算法中初始信息素分布,再利用蚁群算法正反馈机制的特点求精确解,该组合优化算法在克服两种算法缺点的同时发挥了各自的优点,达到优势互补。仿真结果表明,与基本蚁群算法相比,GA-ACO在提高效率的同时改善了解的质量,是可行和有效的。

Aiming to obtain the excellent flight path effectively, a hybrid optimal algorithm, which was based on the well-known advantage and disadvantage of genetic algorithm（GA） and ant colony optimization （ACO） and has maintained the advantage of the two while avoiding the disadvantages, was proposed. In the combinational algprithm process, the global optimizing ability of GA is employed firstly to the quick global search process and after that some optimal solutions, which was used to produce initial Pheromone distribution in ACO, was obtained. Based on this, the ACO, whieh search the optimal solutions by the mechanism of positive feedback, was used subsequently to get the precise solutions. The result of the numerical simulation shows that the hybrid algorithm is effective and can find the more optimal global solutions with high efficiency compared to the basic ACO.