An Efficient Gradient Forecasting Search Method Utilizing the Discrete Difference Equation Prediction Model

摘要

Optimization theory and method profoundly impact numerous engineering designs and applications. The gradient descent method is simpler and more extensively used to solve numerous optimization problems than other search methods. However, the gradient descent method is easily trapped into a local minimum and slowly converges. This work presents a Gradient Forecasting Search Method (GFSM) for enhancing the performance of the gradient descent method in order to resolve optimization problems.