This paper addresses the issues of low convergence accuracy, imbalance between global and local search, and the tendency to get stuck in local optima in the Sea-horse Optimizer. An Improved Sea-horse Optimizer based on a hybrid strategy, denoted as ISHO, is proposed. Firstly, the search characteristics of the Grey Wolf Optimizer are integrated to improve the movement behavior of the SHO, enabling more effective global and local searches within the search space. Then, an elitism and reverse learning strategy is incorporated to refine the search process and enhance convergence accuracy. Finally, adjustments are made to the parameters of the predation phase of the SHO to give it stronger adaptability, avoiding premature convergence to local optima. The ISHO is compared with six other intelligent optimization algorithms on eight test functions. Experimental results show that the proposed algorithm has better convergence speed, accuracy, and stability compared to the other algorithms. Applying the improved seahorse optimization algorithm to solve engineering constraint problems further proves the practicality of the improved algorithm.