ABSTRACT Given the whale optimization algorithm's slow convergence and a tendency to become trapped in local optima, this paper introduces the multiple strategy‐based Whale optimization algorithm (MSWOA) as a solution. In MSWOA, random initialization is used to ensure the population diversity. Dynamic convergence factors and adaptive weight factors are constructed to improve the global and local search capabilities of the algorithm. Then, through the analysis of convergence and complexity, the feasibility of the algorithm is proved. In addition, in order to verify the overall optimization performance of the algorithm and its operability in practical applications, the optimal path of multiple UAVs is determined. Taking the path planning of multiple UAVs under three‐dimensional terrain as the objective optimization problem, the comparison between the proposed algorithm and the other algorithms under the same simulation conditions is done. The MSWOA quickly reaches a stable low objective value within the first 50 iterations, and achieves an average fitness of 0.80, which is about 6% lower than the mean of the other algorithms and about 10% lower than those of WOA and RDWOA. In addition, the standard deviation is reduced by 50% to 70%.
Li et al. (Thu,) studied this question.