Abstract: To address the strong nonlinearity, large disturbances, and slow response inherent in drilling winch drum wire rope tension control systems, this study proposes an ITCM-MOGWO-PSO algorithm that integrates Tent chaotic mapping and a dual-chaotic learning mechanism to optimize PID parameters, thereby achieving high-precision and robust dynamic tension regulation of wire rope. First, a multi-physics field coupled Simulink simulation system was constructed based on the dynamic model of winch wire rope tension. Then, PID parameters were optimized using ITCM-MOGWO-PSO algorithm with the objectives of enhancing tension control accuracy, improving energy consumption smoothness, and reducing tension fluctuation. Simulation results demonstrate that, compared with MOPSO, MOGWO, and MOGWO-PSO, the optimized controller parameters obtained via ITCM-MOGWO-PSO reduce overshoot by 97.7%, 91.7%, and 95.9%, shorten the settling time by 23.9%, 4.1%, and 7.2%, and decrease steady-state error by 1.18%, 0.91%, and 1.09%, respectively. Furthermore, the proposed algorithm achieves faster convergence, yields the lowest average fitness value, and produces a more concentrated solution set distribution, thereby significantly improving the response speed, control accuracy, and robustness of the tension control system, and offers an effective method for intelligent optimization and control of rig hoisting system.