An Efficient Optimization Algorithm for Extreme Value of Nonlinear Function Based on the SAGA and BP Algorithm

A common way to solve the problem of how to obtain the optimal experimental conditions and the experimental results based on limited experimental data is that use of the traditional genetic algorithm optimization BP neural network algorithm (BPGA). However, the experimental results are often unstable and the error of the optimal results is also relatively large. Therefore, we propose a method of sinusoidal adaptive genetic algorithm (SAGA) double optimization BP neural network. The simulation results show that our proposed algorithm effectively improves the accuracy of the extreme value optimization of the nonlinear function. The network prediction error reaches −0.008 to 0.008, the average extreme value is accurate to 0.0084, and the optimal extreme value is accurate to 0.00007.