Multi-population Runge Kutta Optimizer Based on Gaussian Disturbance

Abstract

To address the lack of development capacity of Runge Kutta Optimizer, we propose the Multi-population Runge Kutta algorithm Based on Gaussian disturbance(MPRUN). In the algorithm, the population is divided into subgroups. The individuals in the subgroups are randomly selected for a global search with decreasing search radius with the number of iterations, which is used to improve the global search ability of the subgroups. In addition, the algorithm introduces a Gaussian disturbance mechanism to generate more uniformly distributed populations, performing random perturbation to the global best individual. Finally, the performance of the optimized algorithm is verified by 30 test set functions.