Abstract: An adaptive fast whale optimization algorithm (AWOA) is proposed to solve the problems of insufficient local search ability and slow convergence rate of the standard whale optimization algorithm. The algorithm adaptively selects global search or local search according to the degree of individual distribution and achieves a dynamic balance between them. Levy Flight is introduced for the secondary optimization of individuals with a high degree of deviation from the average position of the sample to further expand the search area and ensure the global search ability of the algorithm. Standard test functions are used to prove that AOWA has high convergence rate and stability. AWOA is applied to unmanned vehicle's path planning. The simulation results show that it has the stable local exploitation capability and global exploration capability. 

Key words: whale optimization algorithm, local exploitation, adaptive, Levy Flight, path planning