Using the developed conjugate gradient method to create an auxiliary function for solving global optimization problems

An alternative approach to solve global optimization challenges is the filled function technique. This study presents a fresh take on the method by proposing the creation of a new filled function that is continuously differentiable and has a single parameter. This approach involves the discovery of an objective function minimizer, which then sets the stage for unbridled global optimization. The proposed filled function approach was shown to be effective in numerical experiments. By repeatedly seeking a superior minimizer in a reduced basin of the filled function, an advanced solution to the objective function can be discovered. This process continues until the optimal solution is reached.