A staged adaptive firefly algorithm for UAV charging planning in wireless sensor networks

Abstract 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.