et al. [4] first used a cat map chaotic sequence to initialize the population, enhancing the randomness and thoroughness of the initial population. Then, they introduced Cauchy mutation and Tent chaotic disturbance to expand local search capabilities, allowing individuals to escape local optima and continue searching. Finally, they proposed an explorer-follower adaptive adjustment strategy to enhance the algorithm's global search capability and local deep search capability. [PERSON] et al. [5] generated the initial population using an elite chaotic reverse learning strategy, improving individual quality and population diversity. They combined the random follower strategy of the chicken swarm algorithm to optimize the follower's position update process, balancing local exploitation performance and global search capability. They employed the Cauchy-Gaussian mutation strategy to enhance population diversity retention and anti-stagnation capabilities. Optimization of 10 benchmark test functions with different characteristics was performed, and results were verified using the Wilcoxon signed-rank test. [PERSON] et al. [6] used Sin chaos to initialize the population, introduced the global optimal solution of the previous generation to improve global search sufficiency, and added adaptive weights to coordinate local exploitation and global exploration capabilities, accelerating convergence speed. They integrated Cauchy mutation operators and reverse learning strategies to perturb and mutate the optimal solution position, enhancing the algorithm's ability to escape local spaces. [PERSON] et al. [7] used an elite reverse learning strategy to improve population diversity and quality and introduced the golden ratio to optimize WOA's optimization method, thereby enhancing the algorithm's convergence speed and stability [6]. The introduction of metaheuristic optimization methods in terminal sliding mode control (SMC) has significant advantages. First, metaheuristic optimization methods can effectively optimize the design parameters of sliding mode controllers, thereby improving the overall performance of the controller, including accuracy and response speed. Since the design of sliding mode controllers usually involves complex non-linear optimization problems, metaheuristic methods are able to find optimal or near-optimal parameter settings in complex search spaces to address these non-linear challenges. At the same time, in the face of system parameter uncertainties, these optimization methods can dynamically adjust controller parameters to improve their robustness and stability. In addition, metaheuristic optimization methods also support adaptive adjustment of controller parameters based on real-time performance feedback, thereby adapting to different operating conditions and environmental changes, improving design efficiency, and reducing design time and cost. These factors combined justify the use of metaheuristic optimization methods in sliding mode controllers to achieve better control effects and higher system performance. To improve the disturbance rejection capability and response speed of terminal sliding mode controllers and to reduce the difficulty of parameter tuning for terminal sliding mode controllers, this paper proposes an improved crayfish algorithm based on multi-strategy integration. This algorithm combines the crayfish algorithm with various optimization strategies to overcome the shortcomings of traditional algorithms in local optima search and convergence speed, thereby achieving parameter optimization of terminal sliding mode controllers in dynamic positioning systems. This paper aims to improve the positioning accuracy and stability of dynamic positioning systems in complex marine environments through the study of the improved crayfish algorithm, providing technical support for the safety and efficiency of marine engineering and offshore operations. This paper integrates multiple optimization strategies into the crayfish algorithm, significantly improving the performance of the algorithm.