Momentum coefficient for promoting accuracy and convergence speed of evolutionary programming

Many practical problems culminate with solving optimization problems. Thus, many methods have been introduced for solving these types of problems. The need for algorithms that are fast and more accurate at finding global minimums is ever increasing. One of the promising methods is a heuristic and iterative method called Evolutionary Programming (EP). It is one of the computational methods used in optimization that is implemented for many practical applications. Many papers have shown the capability of this algorithm for addressing a variety of optimization problems. These studies have opened a vast new and interesting field of research. Recently, many methods have been proposed for promoting the performance of EP when finding the optimum point of functions or applications; however, EP has some shortcomings that cause slow convergence on some functions, especially multimodal functions. By overcoming these shortcomings, EP could be more effective in the optimization research field. This paper introduces new methods for overcoming these disadvantages and promoting the performance of EP. One of these methods, which has the best results on cost functions, changes the searching procedure by adding a new factor to produce offspring and pulling offspring toward a gathering point (the mean value of the parents). This method was tested on 50 well-known test functions discussed in the literature and was compared with state-of-the-art algorithms on twenty-two new cost functions. Finally, a hybrid method of CEP and MCEP (Momentum Coefficient Evolutionary Programming) called IMCEP (Improved Momentum Coefficient Evolutionary Programming) is introduced. The results of the calculations reported here show the efficiency of MCEP and IMCEP.