Abstract: Aiming at the shortcomings of the hunter-prey optimization algorithm, which is easy to fall into local optimum and low convergence precision, a multi-strategy improved hunter-prey optimization algorithm is proposed. The proposed algorithm is based on the idea of dynamic search, and the adaptive adjustment algorithm is transformed from global search to local search; the historical information of the population is used for differential evolution to improve the diversity of the population; combined with the elite pool strategy and nonlinear step size, the algorithm is prevented from falling into local optimum, to improve the convergence accuracy of the algorithm. The improved algorithm and six other classical or state-of-the-art optimization algorithms are evaluated for their performance on 10 large-scale (10 000-dimensional) test functions. The experimental results show that the proposed algorithm has better global optimization capabilities, optimization accuracy and stability, and can effectively solve high-dimensional optimization problems. Finally, the multi-strategy improved hunter-prey optimization algorithm is applied to the path planning problem of 3D UAV. The simulation experiment results show that the algorithm can also solve the optimal result in the 3D path planning optimization problem of UAVs.

Key words: hunter-prey optimization algorithm(HPO), differential evolution, high dimensional optimization, multi-strategy, path planning