针对野马优化算法后期收敛速度慢、搜索精度低、稳定性低等缺陷，提出了一种混合多策略改进野马优化算法。首先，采用Halton序列初始化增加种群多样性；其次，改进自适应参数以平衡全局搜索和局部开发能力；然后，通过单纯形法改善种群个体最差位置；最后，加入躲避行为来提高算法寻优精度。为了验证改进策略的有效性，选择了9个标准测试函数进行仿真实验。将改进算法应用于机械设计问题和桁架结构优化算例中，其优化结果相比原算法降低了16.61%、0.21%、2.96%和0.61%。统计结果表明，改进算法在解决实际工程问题上较基本算法及其他对比具有更高的寻优精度。

Aiming at the defects of late convergence speed, low search accuracy and low stability of wild horse optimizer, a hybrid multi-strategy improved wild horse optimizer was proposed. Firstly, Halton sequence initialization was used to increase population diversity. Secondly, the adaptive parameter was improved to balance the global search and local development capabilities. Then, the worst position of individual population was improved by simplex method. Finally, the escaping behavior was added to improve the optimization accuracy of the algorithm. In order to verify the effectiveness of the improved strategy, 9 standard test functions are selected for simulation experiments. The improved algorithm is applied to mechanical design problems and truss structure optimization examples, and the optimization results are reduced by 16.61%, 0.21%, 2.96% and 0.61% compared with the original algorithm. The statistical results show that the improved algorithm has higher optimization accuracy than the basic algorithm and other comparisons in solving practical engineering problems.

国家自然科学基金资助项目（52278171）；河北省自然科学基金资助项目（E2020402079）；天津大学研究生教育专项基金（C1-2021-004）