Global path planning using modified firefly algorithm

Firefly algorithm is widely used in the tackling of optimization problems. This paper proposed a global path planning algorithm based on the modified firefly algorithm (PPMFA) in order to find an optimal path under multiple objective functions. Owning to the low convergence speed and inaccurate local search ability of the standard firefly algorithm (SFA), the Gaussian random walk is proposed to replace the fixed step size of the SFA so as to improve the random search ability. Incorporating a double check method during the iteration process, the success rate of the movement of each firefly is increased. In order to measure the distance between any two fireflies (which represent two paths), the conception and calculation method of Path Center (PC) is proposed. Simulation results show that compared with the particle swarm optimization (PSO) and SFA, the proposed algorithm outperforms both of the algorithms in terms of convergence speed and accuracy.