This paper proposes a new metaheuristic algorithm called Ship Rescue Optimization (SRO) inspired by the ship maneuvering motion function and the rescue process. The algorithm simulates the ship rescue process, dividing it into two types: delayed rescue (large area rescue) and immediate rescue (small rescue). These two types of rescue behaviors are mapped to the search space exploration and exploitation processes, respectively. The SRO algorithm is designed to find an optimal position update algorithm based on the ship maneuvering equation of motion. The algorithm is tested on 57 test functions from CEC2013 and CEC2017, as well as three real engineering problems, and compared with eight current mainstream algorithms. The results show that SRO is robust and effective in solving challenging optimization problems. The algorithm's performance is evaluated in terms of convergence ability and is shown to be superior in high-dimensional problems. The SRO algorithm is also applied to three real-world engineering problems, including tubular column design, pressure vessel design, and welded beam design. The results demonstrate that SRO achieves good performance in terms of both the mean and mean squared deviation values of the optimal solutions. The algorithm is shown to have strong convergence performance and can avoid falling into local optima. The paper concludes that SRO is a promising metaheuristic algorithm for solving optimization problems.This paper proposes a new metaheuristic algorithm called Ship Rescue Optimization (SRO) inspired by the ship maneuvering motion function and the rescue process. The algorithm simulates the ship rescue process, dividing it into two types: delayed rescue (large area rescue) and immediate rescue (small rescue). These two types of rescue behaviors are mapped to the search space exploration and exploitation processes, respectively. The SRO algorithm is designed to find an optimal position update algorithm based on the ship maneuvering equation of motion. The algorithm is tested on 57 test functions from CEC2013 and CEC2017, as well as three real engineering problems, and compared with eight current mainstream algorithms. The results show that SRO is robust and effective in solving challenging optimization problems. The algorithm's performance is evaluated in terms of convergence ability and is shown to be superior in high-dimensional problems. The SRO algorithm is also applied to three real-world engineering problems, including tubular column design, pressure vessel design, and welded beam design. The results demonstrate that SRO achieves good performance in terms of both the mean and mean squared deviation values of the optimal solutions. The algorithm is shown to have strong convergence performance and can avoid falling into local optima. The paper concludes that SRO is a promising metaheuristic algorithm for solving optimization problems.