基于自适应飞蛾扑火优化算法的三维路径规划

三维路径规划问题是在干扰环境下寻找出发点到目的地之间最优路径的组合优化问题。针对传统群智能算法在求解该问题时存在收敛精度低、易陷入局部最优等缺陷，提出了一种自适应飞蛾扑火优化算法对该问题进行优化求解。改进算法通过引入飞行方向动态调整策略和位置交叉策略，在动态调整飞蛾飞行方向的同时不断产生新个体，有效避免了算法陷入局部最优；通过自适应调整火焰的数量，在算法全局探索阶段增强了种群多样性，避免了早熟收敛。将自适应飞蛾扑火优化算法与其他群智能算法用于三维路径规划问题求解，实验结果表明，改进的自适应飞蛾扑火优化算法在所有算法中代价值最小，收敛速度最快，说明该算法在三维路径规划问题中具有更好的求解能力。

3D path planning based on adaptive moth fire fighting optimization algorithm

The three-dimensional path planning problem is a combinatorial optimization problem to find the optimal path between the starting point and the destination in the interference environment. Aiming at the shortcomings of traditional swarm intelligence algorithm in solving this problem, such as low convergence accuracy and easy to fall into local optimum, this paper proposed an adaptive moth-flame optimization algorithm to solve the problem. By introducing the dynamic adjustment strategy of the flight direction and the position intersection strategy, the improved algorithm continuously generated new individuals while dynamically adjusting the flight direction of the moth, which effectively avoided the algorithm from falling into local optimum; by adaptively adjusting the number of flames, in the global exploration stage of the algorithm enhanced population diversity and avoided premature convergence. It used the adaptive moth-flame optimization algorithm and other swarm intelligence algorithms to solve the three-dimensional path planning problem. The experimental results show that the improved adaptive moth-flame optimization algorithm has the smallest cost value and the fastest convergence speed among all algorithms. The algorithm has better solution ability in 3D path planning problem.