Multi‐Strategy Synergy‐Based Differential Evolution for Large Wind Farm Layout Optimization

ABSTRACT Exploring clean energy alternatives to fossil fuels has become a major research focus worldwide. Wind energy is regarded as one of the most promising renewable energy sources due to its clean and sustainable nature. However, wake interactions among turbines significantly reduce power conversion efficiency, making wind farm layout optimization (WFLO) crucial for maximising energy output. As the number of turbines increases, wake interactions become more pronounced, further deteriorating overall efficiency. Metaheuristic algorithms have been widely adopted to address the complex constraints and design objectives of WFLO. Nevertheless, traditional heuristic methods often suffer from poor solution quality and premature convergence when dealing with large‐scale WFLO problems under complex wind conditions. To overcome these limitations, this study proposes a multi‐strategy synergy‐based differential evolution algorithm (LSDE) for large‐scale WFLO under complex wind scenarios. The proposed method is evaluated against nine representative WFLO algorithms under four complex wind scenarios (4, 5, 6 and 7 wind directions) and three turbine scales (30, 50 and 100 turbines). Experimental results demonstrate that LSDE achieves superior performance, stability and robustness. Specifically, LSDE improves power conversion efficiency by 3.40%, 3.51%, 3.10% and 3.36% under the four wind scenarios, respectively, compared with other mainstream algorithms.