A staged adaptive firefly algorithm (SAFA) is proposed in this paper. Firstly, the attraction model is improved to promote the convergence of the algorithm in the case of small algorithm complexity. Secondly, three adaptive adjustment functions of parameters are established according to the actual conditions of convergence and iteration. Because of the new attraction model, SAFA has better population diversity at the early stage of iteration and can carry out adaptive balance and adjustment of global and local optimization at the late stage of iteration. Because of three adaptive adjustment functions of parameters, SAFA has better randomness and non-repeatability of parameters, so it has stronger global convergence ability. To verify the performance, SAFA algorithm is compared with other four algorithms in testing six standard functions and unmanned aerial vehicle (UAV) charging path planning for wireless sensor network in this paper. A large number of experimental results show that the precision and convergence speed of SAFA is higher than that of the other four algorithms.

提出了一种阶段性自适应萤火虫算法（SAFA）。首先，在算法复杂度较小的情况下，对吸引模型进行了改进，以促进算法的收敛。其次，根据收敛和迭代的实际情况，建立了三个自适应参数调整函数。由于采用了新的吸引力模型，SAFA在迭代的早期具有更好的种群多样性，并且可以在迭代的后期进行自适应平衡以及全局和局部优化的调整。由于SAFA具有三个自适应的参数调整功能，因此SAFA具有较好的参数随机性和不可重复性，因此具有较强的全局收敛能力。为了验证性能，在测试六个标准功能和无线传感器网络的无人机充电路径规划中，将SAFA算法与其他四个算法进行了比较。大量实验结果表明，SAFA的精度和收敛速度均高于其他四种算法。