集成自适应变异混沌松鼠搜索和 LSTM 算法的RUL 预测方法及应用

针对松鼠搜索算法(SSA)优化长短期记忆人工神经网络(LSTM)时，存在优化参数易陷入局部最优以及LSTM预测效率下降的问题，提出一种自适应变异混沌松鼠搜索算法(AMCSSA)优化LSTM学习率及其下降因子的预测模型。通过计算AMCSSA的时间复杂度证明其在未增加算法复杂度的前提下提高寻优效率，AMCSSA采用切比雪夫混沌映射生成混沌初始种群，并将捕食者概率改为非线性递减模式，利用位置贪婪选择策略使其在算法迭代过程中不断更新并保留更优个体，引入自适应T变异策略提高SSA在搜索空间中的勘探能力。通过AMCSSA对LSTM的学习率及其下降因子进行参数寻优，进一步提高LSTM的预测能力。对滚动轴承的剩余使用寿命(RUL)进行实验验证，结果表明所提方法相较于传统SSA、粒子群算法(PSO)、蝙蝠算法(BAT)以及萤火虫算法(FA)优化LSTM后，在预测中的精度分别提高了1.05%、7.61%、8.4%以及7.73%,并且使优化后的LSTM在完成收敛所需要的迭代次数减少，从而提高预测效率。

Prediction of RUL and application of the integrated squirrel search algorithm with adaptive mutation chaos for LSTM

A squirrel search algorithm with adaptive mutation chaos (AMCSSA) is proposed in this paper to improve the learning rate of LSTM and the prediction pattern of its decay factor. This is to solve issues of the tendency of local optimal for optimized parameters and the decay of LSTM prediction efficiency during the optimization of LSTM with SSA. By calculating its time complexity, AMCSSA is proved to be able to increase the searching efficiency without increasing the complexity of the algorithm. AMCSSA adopts Chebyshev chaotic map to generate the chaotic initial population, and switches to use a nonlinear decreasing for the predator probability. The positional greedy selection strategy is used to continuously update and keep individuals of more advantages during the iteration of algorithms, then the adaptive T mutant is introduced to improve SSA''s exploration capabilities in searching space. AMCSSA optimizes the parameters for the learning rate of LSTM and their decay factors, thus the predictive ability of LSTM is further improved. This is verified by experiments on the remaining useful life of rolling bearing. The results show that the prediction accuracy of LSTM optimized by AMCSSA increased by 1. 05%, 7. 61%, 8. 4%, and 7. 73%, respectively, compared to those optimized by traditional SSA, particle swarm optimization (PSO), bat algorithm (BAT), and firefly algorithm (FA). With the proposed algorithm, the number of iteration required for the optimized LSTM to complete the convergence is also reduced, so that the prediction efficiency is increased.