Enhanced differential evolution with adaptive direction information

Most recently, a DE framework with neighborhood and direction information (NDi-DE) was proposed to exploit the information of population and was demonstrated to be effective for most of the DE variants. However, the performance of NDi-DE heavily depends on the selection of direction information. In order to alleviate this problem, two adaptive operator selection (AOS) mechanisms are introduced to adaptively select the most suitable type of direction information for the specific mutation strategy during the evolutionary process. The new method is named as adaptive direction information based NDi-DE (aNDi-DE). In this way, the good balance between exploration and exploitation can be dynamically achieved. To evaluate the effectiveness of aNDi-DE, the proposed method is applied to the well-known DE/rand/1 algorithm. Through the experimental study, we show that aNDi-DE can effectively improve the efficiency and robustness of NDi-DE.