<p>Over the past few decades, the simplicity and flexibility of swarm-inspired algorithms have captivated the attention of many researchers. In this study, a recently designed swarm-based meta-heuristic, namely "Sea Horse Optimizer" (SHO), is introduced, which draws inspiration from the natural mobility, predation, and breeding activities of sea horses for addressing numerous complex problems. However, similar to other methodologies, SHO exhibits shortcomings in terms of exploitation ability, balancing exploration and exploitation, and susceptibility to local optima. To alleviate these limitations, this paper introduces a novel enhancement to SHO utilizing an innovative strategy: enhanced opposition-based learning (EOBL), which is deeply inspired by opposition-based learning (OBL) and is employed to efficiently explore the search region. The resulting model, termed the Enhanced Opposition-based Sea Horse Optimizer (EOBSHO), significantly enhances optimization precision and convergence speed compared to SHO. Additionally, other models, like OBSHO, are introduced for comparative analysis. Through experiments and statistical tests, like the Wilcoxon rank test, the proposed EOBSHO consistently outperforms SHO, OBSHO, and other state-of-the-art algorithms. The algorithm’s robustness is validated using benchmark functions, and its efficacy is further demonstrated in addressing real engineering problems, such as the tension/compression spring design problem and gear train problem. The results across benchmark functions along with engineering problems affirm that EOBSHO excels in both unconstrained and constrained problem domains. The source code is available as <a href="https://data.mendeley.com/datasets/gw2rf7ssxp/1">https://data.mendeley.com/datasets/gw2rf7ssxp/1</a></p>