Abstract: Addressing the issues of slow convergence speed and the tendency to fall into local optima in the Hunter-Prey Optimizer (HPO), a multi-strategy improved hunter-prey optimization algorithm (IHPO) is proposed. Firstly, the good point set is used to initialize the population to enhance the diversity of the population. Secondly, the nonlinear control parameter strategy is introduced to optimize search, develop balance parameters, adjust global-local searching weights, and improve the convergence speed. Then, the Levy flight strategy and the greedy strategy are introduced to update the hunter position, which make it possible for the population to jump out of the local optimal, and the golden sine strategy is introduced to update the prey position and improve the local exploitation ability. The benchmark functions are used for optimization comparison, and the Wilcoxon rank sum test between IHPO and other six intelligent algorithms is used. The simulation results show that IHPO has better optimization ability and convergence speed. Finally, IHPO is applied to two practical engineering optimization problems, and the simulation results show that IHPO has good applicability and stability in solving engineering optimization problem.

Key words: hunter-prey optimization, good point set, nonlinear search and development of balance parameters, levy flight strategy, greedy strategy, golden sine strategy