Differential Analysis of the Application of Evolutionary Strategies in Reservoir Operating Recurrent Neural Networks

Abstract

Recurrent neural networks (RNNs) are widely used in reservoir operation because they can represent temporal dependencies, but their performance depends strongly on hyperparameter selection. This study compares eight evolutionary strategies—genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), differential evolution (DE), ant colony optimization (ACO), artificial electric field algorithm (AEFA), slime mould algorithm (SMA), and white shark optimization (WSO)—for optimizing the hidden-node number and initial learning rate of long short-term memory (LSTM) and gated recurrent unit (GRU) models. Under common experimental settings, ACO required the shortest computation time in all reported dataset–architecture blocks, whereas the most accurate strategy depended on the dataset, architecture, and metric. For the total dataset, ACO–LSTM and AEFA–GRU produced the lowest reported MAEs (609.876 and 582.942 m3/s, respectively); WSO–LSTM was best for flood-season MAE (948.326 m3/s), and GA–GRU was best for non-flood-season MAE (516.088 m3/s). An independent chronological holdout experiment further verifies the search boundaries and validation-fitness procedure and shows that the fitness response also varies with hydrological period and RNN architecture. The results support objective- and dataset-specific optimizer selection, subject to the limitations of a single-reservoir case study and a restricted two-variable search.