The parameter adjustment of the washing out algorithm has a great influence on the washing out effect. Aiming at the deficiency of the parameter adjustment of the classical washing out algorithm, an improved multi-objective slime mold algorithm is proposed to find the optimal parameter structure through this algorithm. Aiming at the initialization and local optimization problems of the standard slime mold algorithm, as well as the limited function optimization effect of solutions at non-origin, the Circle chaotic elite initialization population method was introduced, Levi Flight was introduced into the early position update formula of the standard slime mold algorithm, and the algorithm based on acquisition and shared knowledge was introduced into the later update formula, and the slime mold local optimization strategy was designed. Multiple objective functions are constructed by introducing Pareto non-dominated ordering. Human perception model, acceleration difference model and displacement model are established as objective functions, and their parameters are optimized. Finally, it is verified by simulation and experiment. The results show that the phase delay is improved by 41.7%, the working space of the motion platform is saved by 43.58%, and the washing effect is effectively improved.