Mixed non linear programming model for optimizing and training long short-term memory architectures: application in traffic flow forecasting
It is demonstrated that framing LSTM architecture optimization as a mixed-variable combinatorial problem, coupled with PSO-based optimization, substantially improves forecasting performance, offering a robust and versatile strategy for accurate traffic prediction and other complex sequential data applications.