Hybrid Optimization Approach: Improving BPNN with Refined EVO Algorithm for Multi-Input Nonlinear Systems

The study presents a new optimization method that merges Backpropagation Neural Networks (BPNN) with an enhanced Energy Valley Optimizer (EVO) algorithm to elevate the performance of multi-input nonlinear systems. Traditional optimization methods like Gradient Descent and Particle Swarm Optimization (PSO) face issues with slow convergence and local minima entrapment which the hybrid model successfully resolves. The exploitation of EVO's innate energy dissipation mechanism results in faster BPNN convergence and improved generalization capabilities. Experiments with both synthetic and real-world datasets proved that the hybrid model showed better accuracy and prediction capabilities along with faster convergence time. The findings demonstrate substantial decreases in Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) relative to conventional optimization methods. The sensitivity analysis demonstrates that optimal EVO performance depends on precise adjustments of parameters including mutation rate and local search operator. This study presents an efficient method to enhance BPNN training which shows promise in managing complex nonlinear systems.