Self-adaptive barebones differential evolution

Differential evolution (DE) is generally considered as a reliable, accurate, robust and fast optimization technique. DE has been successfully applied to solve a wide range of numerical optimization problems. However, the user is required to set the values of the control parameters of DE for each problem. Such parameter tuning is a time consuming task. In this paper, a new version of DE which eliminates the need for manual parameter tuning is proposed. The performance of the proposed approach is investigated and compared with other well-known approaches. The results show that the new algorithm provides good performance when applied to multimodal problems with the added advantage that no parameter tuning is needed.