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

Yiliang Wang, Yifei Yang, Sichen Tao (), Lianzhi Qi and Hao Shen
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Yiliang Wang: School of Mechanical Engineering, Tiangong University, Tianjin 300387, China
Yifei Yang: Faculty of Science and Technology, Hirosaki University, Hirosaki 036-8560, Japan
Sichen Tao: Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan
Lianzhi Qi: School of Mechanical Engineering, Tiangong University, Tianjin 300387, China
Hao Shen: Tiangong Innovation School, Tiangong University, Tianjin 300387, China

Mathematics, 2025, vol. 13, issue 18, 1-34

Abstract: 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.

Keywords: sustainable energy; wind farm layout optimization; differential evolution; genetic learning competitive elimination strategy; genetic algorithm; particle swarm optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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