<p>The Sea Horse Optimization (SHO) algorithm, introduced in 2022, is a novel metaheuristic inspired by the foraging behavior of sea horses.Although the Sea Horse Optimization algorithm can solve most optimization problems, it has a slow convergence speed and is prone to falling into local optima. To address these issues, this paper presents an Enhanced Sea Horse Optimization (ESHO) algorithm, which incorporates three key improvements: Logistic Chaotic Mapping, an Adaptive Multiple Mutation Strategy (AMMS), and an Adaptive Restart Mechanism (ARM). First, Logistic Chaotic Mapping is employed to initialize the population, enhancing diversity and speeding up convergence. Second, AMMS replaces the reproduction phase of SHO, improving exploration by adaptively modifying mutation strategies to achieve a better balance between exploration and exploitation. Third, ARM is introduced to help the algorithm escape local optima by generating new positions when stagnation occurs. The effectiveness of ESHO is validated on the CEC2014 and CEC2020 benchmark sets, as well as on seven engineering optimization problems, with comparisons made against several well-established optimization algorithms. The experimental results show that ESHO ranks first on 21 test functions of CEC2014 and seven of CEC2020. The Wilcoxon signed—rank test is used on CEC2014 and CEC2020. It shows that on CEC2014, ESHO outperforms SHO on 27 functions and is almost equal on three. On CEC2020, ESHO is better on seven functions and similar on three. Additionally, ESHO ranks first in seven real—world engineering problems, with better solutions, means, and smaller standard deviations than competing algorithms. Overall, ESHO emerges as a highly competitive and effective optimization algorithm capable of solving both benchmark and real-world engineering optimization problems. The source code is available from the following source: <a href="https://github.com/hellowjx/ESHO">https://github.com/hellowjx/ESHO</a></p>