This paper presents a differential evolution algorithm with a new encoding mechanism for efficiently solving the optimal layout of the wind farm, with the aim of maximizing the power output. In the modeling of the wind farm, the wake effects among different wind turbines are considered and the Weibull distribution is employed to estimate the wind speed distribution. In the process of evolution, a new encoding mechanism for the locations of wind turbines is designed based on the characteristics of the wind farm layout. This encoding mechanism is the first attempt to treat the location of each wind turbine as an individual. As a result, the whole population represents a layout. Compared with the traditional encoding, the advantages of this encoding mechanism are twofold: 1) the dimension of the search space is reduced to two, and 2) a crucial parameter (i.e., the population size) is eliminated. In addition, differential evolution serves as the search engine and the caching technique is adopted to enhance the computational efficiency. The comparative analysis between the proposed method and seven other state-of-the-art methods is conducted based on two wind scenarios. The experimental results indicate that the proposed method is able to obtain the best overall performance, in terms of the power output and execution time.Index Terms-Differential evolution (DE), encoding mechanism, optimization, wake effect, wind farm layout.Hao Liu received the B.S. degree in automation in 2015 from Central South University, Changsha, China, where he is currently working toward the M.S. degree in control science and engineering.His research interests include real-world applications of computational intelligence and machine learning.Huan Long (S'15) received the B.S. degree from the in 2017.Her research interests include data mining and computational intelligence applied in the renewable energy optimization, such as wind farm layout, hybrid renewable system configuration, renewable energy prediction, and wind turbine monitoring. . His research focuses on data mining and computational intelligence with applications in wind energy, HVAC and wastewater processing domains.Shengxiang Yang (M'00-SM'14) received the B.Sc. and M.Sc. degrees in automatic control and the Ph.D. degree in systems engineering He has more than 230 publications. His current research interests include evolutionary computation, swarm intelligence, computational intelligence in dynamic and uncertain environments, artificial neural networks for scheduling, and relevant real-world applications.Dr. Yang serves as an Associate Editor or Editorial Board Member of eight international journals, such as the IEEE TRANSACTIONS ON CYBER-