Sea Horse Optimizer (SHO) is a noteworthy metaheuristic algorithm that emulates various intelligent behaviors exhibited by sea horses, encompassing feeding patterns, male reproductive strategies, and intricate movement patterns. To mimic the nuanced locomotion of sea horses, SHO integrates the logarithmic helical equation and Levy flight, effectively incorporating both random movements with substantial step sizes and refined local exploitation. Additionally, the utilization of Brownian motion facilitates a more comprehensive exploration of the search space. This study introduces a robust and high-performance variant of the SHO algorithm named mSHO. The enhancement primarily focuses on bolstering SHO's exploitation capabilities by replacing its original method with an innovative local search strategy encompassing three distinct steps: a neighborhood-based local search, a global non-neighbor-based search, and a method involving circumnavigation of the existing search region. These techniques improve mSHO algorithm's search capabilities, allowing it to navigate the search space and converge toward optimal solutions efficiently. The comprehensive results distinctly establish the supremacy and efficiency of the mSHO method as an exemplary tool for tackling an array of optimization quandaries. The results show that the proposed mSHO algorithm has a total rank of 1 for CEC'2020 test functions. In contrast, the mSHO achieved the best value for the engineering problems, recording a value of 0.012665, 2993.634, 0.01266, 1.724967, 263.8915, 0.032255, 58507.14, 1.339956, and 0.23524 for the pressure vessel design, speed reducer design, tension/compression spring, welded beam design, three-bar truss engineering design, industrial refrigeration system, multi-Product batch plant, cantilever beam problem, multiple disc clutch brake problems, respectively.

翻译：海马优化器（SHO）是一种值得关注的元启发式算法，它模拟海马展现的多种智能行为，包括摄食模式、雄性繁殖策略及复杂的运动模式。为模仿海马精细的运动机制，SHO融合了对数螺旋方程与莱维飞行，有效结合了大步长随机移动与精细局部开发。此外，布朗运动的运用促进了对搜索空间更全面的探索。本研究提出一种名为mSHO的鲁棒高性能SHO算法变体。其改进主要聚焦于增强SHO的开发能力：用创新的三步局部搜索策略替代原始方法，包括基于邻域的局部搜索、基于全局非邻居的搜索以及围绕现有搜索区域的环绕搜索方法。这些技术提升了mSHO算法的搜索能力，使其能高效遍历搜索空间并收敛至最优解。综合结果明确证明了mSHO方法作为解决各类优化问题的典范工具的优越性与高效性。结果显示，所提mSHO算法在CEC2020测试函数中总排名第一。对于工程问题，mSHO分别在压力容器设计、减速器设计、拉压弹簧、焊接梁设计、三杆桁架工程设计、工业制冷系统、多产品批处理厂、悬臂梁问题、多盘离合器制动问题中取得最优值，分别为0.012665、2993.634、0.01266、1.724967、263.8915、0.032255、58507.14、1.339956和0.23524。