Research on Construction and Performance Optimization of the LEA-LSTM Model

Wen-Min Wen, Yan-Zuo Chang, Jin-Ping Chen, Hong-Rui Yang, Yong-Qing Wang, Yu-Xuan Chen, Jie-Zhen Yang, Guan-Hong Xie, Zi-Rui He, Zheng-Kuan Deng, Kai-Ming Chen

LEA-LSTM, time series, LSTM, meta-heuristic optimization algorithm

Aiming at the problems of the Long Short-Term Memory network (LSTM) in time series modeling, such as hyperparameter adjustment relying on experience, being prone to falling into local optimum, and slow convergence speed, an LSTM model optimized by the Love Evolution Algorithm (LEA), namely LEA-LSTM, is proposed. First, the gating mechanism and time series processing principle of the LSTM network are elaborated, and the influence of its core hyperparameters on model performance is analyzed. Second, the LEA algorithm is introduced, and the adaptive optimization of the key hyperparameters of LSTM is realized through the five-stage evolution mechanism of encounter, stimulation, reflection, value and role, which solves the defect of insufficient global search capability of traditional optimization algorithms. Finally, the Jena Climate Dataset, a general time series dataset, and scenario-specific dataset such as power load are used for performance verification. The proposed model is compared with LSTM, PSO-LSTM, WOA-LSTM, BWO-LSTM and IGWA-ADConv1D-LSTM models in three aspects: prediction accuracy, convergence speed and robustness. The results show that the Mean Absolute Error (MAE) of the LEA-LSTM model on the Jena Climate Dataset is reduced by 68.3% compared with LSTM, and by 42.1%, 37.5% and 29.8% compared with PSO-LSTM, WOA-LSTM and BWO-LSTM respectively; in the power load forecasting scenario, the MAE is reduced by 18.6% compared with IGWA-ADConv1D-LSTM; the convergence speed is increased by more than 35% compared with traditional optimized models, and the coefficient of determination (R²) remains 99.1% even in small sample scenarios.

Received: 26 Feb 2026, Received in revised form: 28 Mar 2026, Accepted: 03 Apr 2026, Available online: 07 Apr 2026

10.22161/ijaers.134.5