A Reinforcement Learning-Assisted Fractional-Order Differential Evolution for Solving Wind Farm Layout Optimization Problems

The Wind Farm Layout Optimization Problem (WFLOP) aims to improve wind energy utilization and reduce wake-induced power losses through optimal placement of wind turbines. Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been widely adopted due to their suitability for discrete optimization tasks, yet they suffer from limited global exploration and insufficient convergence depth. Differential evolution (DE), while effective in continuous optimization, lacks adaptability in discrete and nonlinear scenarios such as WFLOP. To address this, the fractional-order differential evolution (FODE) algorithm introduces a memory-based difference mechanism that significantly enhances search diversity and robustness. Building upon FODE, this paper proposes FQFODE, which incorporates reinforcement learning to enable adaptive adjustment of the evolutionary process. Specifically, a Q-learning mechanism is employed to dynamically guide key search behaviors, allowing the algorithm to flexibly balance exploration and exploitation based on problem complexity. Experiments conducted across WFLOP benchmarks involving three turbine quantities and five wind condition settings show that FQFODE outperforms current mainstream GA-, PSO-, and DE-based optimizers in both solution quality and stability. These results demonstrate that embedding reinforcement learning strategies into differential frameworks is an effective approach for solving complex combinatorial optimization problems in renewable energy systems.