Recent years have witnessed an enormous growth of wind farm capacity worldwide. Due to the wake effect, the velocity of incoming wind is reduced for the wind turbines in the downwind directions, thus causing discounted power generation in a wind farm. Previously, a self-informed adaptivity mechanism in evolutionary algorithms was introduced by the authors, which is inspired by the individuals’ self-adaptive capability to fit the environment in the natural world, where relocating the worst wind turbine with a surrogate model informed mechanism was found to be effective in improving the power conversion efficiency. In this paper, the exploitation capability in the adaptive genetic algorithm is further improved by casting the relocation of multiple wind turbines into a single-player reinforcement learning problem, which is further addressed by Monte-Carlo Tree Search embedded within the evolutionary algorithm. In contrast to the moderate improvements of the authors’ previous algorithms, significant improvement is achieved due to the enhanced algorithmic exploitation. The new algorithm is also applied to solve the optimal layout problem for a recently approved wind farm in New Jersey, and showed better performance against the benchmark algorithms.

近年来，全球风电场容量大幅增长。由于尾流效应，下风向的风力涡轮机的来风速度降低，从而导致风场发电的折扣。此前，作者在进化算法中引入了一种自我通知的自适应机制，该机制受到个体适应自然界环境的自适应能力的启发，其中用代理模型通知机制重新定位最差的风力涡轮机是被发现可以有效地提高功率转换效率。在本文中，通过将多台风力发电机的重定位转化为单人强化学习问题，进一步提高了自适应遗传算法的开发能力，嵌入在进化算法中的蒙特卡罗树搜索进一步解决了这个问题。与作者先前算法的适度改进相比，由于增强的算法开发，实现了显着的改进。新算法还用于解决新泽西州最近批准的风电场的优化布局问题，并且与基准算法相比表现出更好的性能。