Solution searching for multivariable optimization problems by a momentum genetic algorithm

Genetic algorithms (GAs) have emerged as powerful solution searching mechanisms, especially for nonlinear and multivariable optimization problems. Generally, it is time-consuming for GAs to find the solutions, and sometimes they cannot find the global optima. In order to improve their search performance, we propose a fast GA algorithm called momentum GA, which employs momentum offspring (MOS) and constant range mutation (CRM). MOS, which generates offspring based on the best individuals of current and past generations, is considered to have the effect of fast searching for the optimum solutions. CRM is considered to have the ability to avoid the production of ineffective individuals and maintain the diversity of the population. In order to verify the performance of our proposed method, a comparison between momentum GA and the conventional mean will be implemented by utilizing optimization problems of two multivariable functions and neural network training problems with different activation functions. Simulations show that the proposed method has good performance regardless of the small values of the population size and generation number in the GA.