DE-AEC: A differential evolution algorithm based on adaptive evolution control

A new differential evolution algorithm DE-AEC, is proposed based on adaptive evolution control utilizing the information provided by a surrogate model. The algorithm is useful for optimization problems with expensive function evaluations, because it can significantly reduce the number of true function evaluations. Specifically DE-AEC generates multiple offspring for each parent and chooses the promising one based on the accuracy and the predicted function value of the current surrogate model. The model´s accuracy is also used as an indicator of potential false convergence and special measures are taken to improve the convergence reliability. Simulation results on a set of fifteen test functions show that, compared to an already improved DE algorithm DE-AEC reduces the number of true function evaluations by 30%-80% for fourteen functions in the achievement of either low-level (10^-2 ) or high-level (10^-8 ) accuracy.