针对飞机模拟器运动平台经典洗出算法参数选取不当，对动感逼真度产生较大误差，影响模拟效果的情况，提出了一种融合模拟退火和自适应变异的改进鲸鱼优化算法。结合人体前庭系统，建立人体感知仿真模型，将动感逼真度问题转换为寻找洗出算法滤波器最优参数的问题。通过测试函数证明改进鲸鱼算法相较于传统鲸鱼算法，以及与常见的粒子群算法和遗传算法，具有更好的搜索效果。此外仿真实验也表明，与经典洗出算法和传统鲸鱼优化算法结果相比，改进鲸鱼优化算法收敛速度最快，洗出时间分别缩短了83.3%和33.4%，相比优化了45.2%和26.1%的运动空间。证明了改进后鲸鱼算法具有较强的鲁棒性，运动洗出模拟效果明显提升。

Aiming at the situation that the parameters of the classical washout algorithm of the motion platform of the aircraft simulator are not selected properly, which causes a large error to the dynamic fidelity and affects the simulation effect, an improved whale optimization algorithm combining simulated annealing and adaptive variation is proposed. Combining with the vestibular system, a human perception simulation model is established, and the dynamic fidelity problem is transformed into the problem of finding the optimal parameters of the filter of the washout algorithm. The test function proves that the improved whale algorithm has better searching effect than traditional whale algorithm, particle swarm optimization algorithm and genetic algorithm. In addition, the simulation experiment also shows that compared with the classical washout algorithm and the traditional whale optimization algorithm, the improved whale optimization algorithm had the fastest convergence speed, the washout time was shortened by 83.3% and 33.4%, respectively, and the motion space was optimized by 45.2% and 26.1%. It is proved that the improved whale algorithm has strong robustness, and the simulation effect of motion washout is obviously improved.