Abstract: To address issues such as slow convergence speed, low convergence accuracy, and proneness to falling into local optima in the mountain gazelle optimizer (MGO), a multi-strategy improved mountain gazelle optimizer algorithm(MSIMGO) is proposed. Firstly, a good point set is used to initialize the population, improving the quality of the initial population. Secondly, the golden sine strategy is integrated to enhance the convergence speed. Thirdly, the vortex effect is introduced to alleviate the reduction in population diversity in the later stage. Finally, Cauchy mutation is applied to perturb the position of the optimal gazelle, enhancing the algorithm’s ability to escape local optima. Comparative optimization experiments with 8 other algorithms on 8 benchmark  functions and CEC2019 benchmark functions show that MSIMGO has stronger optimization capability, and the effectiveness of MSIMGO is validated through the Wilcoxon rank-sum test. The application of MSIMGO to the engineering problem of pressure vessel design demonstrates its feasibility and effectiveness in handling practical engineering problems.

Key words: mountain gazelle optimizer, good point set, golden sine strategy, eddy current effect, Cauchy mutation