Bio-Inspired Metaheuristic Optimization of a DWT-BiLSTM Architecture for Wind Speed Forecasting: A Statistical Benchmark with Component Ablation.

Population-based bio-inspired metaheuristics are the dominant tools for tuning hybrid decomposition-deep-learning forecasters, yet their relative behavior on a common problem is rarely assessed with a leakage-free, physically meaningful protocol. We benchmark eight metaheuristics on the joint nine-dimensional hyperparameter optimization of a discrete-wavelet-transform bidirectional-LSTM (DWT-BiLSTM) architecture for short-term wind speed forecasting, using 409,152 hourly observations from eight meteorological stations. The set comprises six nature-inspired methods (Artificial Bee Colony, ABC; genetic algorithm, GA; Particle Swarm Optimization, PSO; Grey Wolf Optimizer, GWO; Hippopotamus Optimization, HO; and the Raindrop Optimizer) together with two recent metaphor-free or social variants (the Farthest-better Nearest-worse Optimizer, FNO; and the Tuckman Optimization Algorithm, TOA). A multi-stage protocol covers 30 independent runs per algorithm, a joint-versus-sequential comparison, a genuine rolling-origin out-of-sample evaluation, and component ablation. Friedman testing reveals significant differences (χ