Differential Evolution Algorithms Used to Optimize Weights of Neural Network Solving Pole-Balancing Problem

摘要

Differential evolution (DE) has been successfully used to solve difficult optimization problems. Every year, novel DE algorithms are developed to outperform the previous versions. The JADE is a famous DE algorithm using a mutation strategy current-to-pbest and the adaptation of control parameters. The SHADE has been developed to eliminate some bottlenecks of the JADE, especially its tendency to a premature convergence. The performance of these algorithms has been demonstrated on various benchmarks. The goal of this work is to compare the performance of the selected DE algorithms which are used to optimize the weights of the artificial neural network solving the pole-balancing problem.