DE-AEC: A differential evolution algorithm based on adaptive evolution control
Jingqiao Zhang, Arthur C. Sanderson · 2007
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.