Advanced 3D Wind Farm Layout Optimization Framework via Power-Law Perturbation-Based Genetic Algorithm

Jiaru Yang; Yaotong Song; Jun Tang; Weiping Ding; Zhenyu Lei; Shangce Gao

doi:10.1109/JAS.2025.125351

Volume 12 Issue 11

Nov. 2025

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2025 > 12(11): 2314-2328

J. Yang, Y. Song, J. Tang, W. Ding, Z. Lei, and S. Gao, “Advanced 3D wind farm layout optimization framework via power-law perturbation-based genetic algorithm,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 11, pp. 2314–2328, Nov. 2025. doi: 10.1109/JAS.2025.125351

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J. Yang, Y. Song, J. Tang, W. Ding, Z. Lei, and S. Gao, “Advanced 3D wind farm layout optimization framework via power-law perturbation-based genetic algorithm,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 11, pp. 2314–2328, Nov. 2025. doi: 10.1109/JAS.2025.125351

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Advanced 3D Wind Farm Layout Optimization Framework via Power-Law Perturbation-Based Genetic Algorithm

doi: 10.1109/JAS.2025.125351

Funds: This work was partially supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (JP23K24899) and Japan Science and Technology Agency (JST) Support for Pioneering Research Initiated by the Next Generation (SPRING) (JPMJSP2145)

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Author Bio:
Jiaru Yang received the M.E. degree and the Ph.D. degree from the University of Toyama, Toyama, Japan, in 2022 and 2025, respectively. His current research interests include optimization theory, computational intelligence, and machine learning

Yaotong Song received the B.S. degree from Beijing University of Civil Engineering and Architecture. He is currently pursuing the M.E. degree from the University of Toyama, Toyama, Japan. His current research interests include computational intelligence and neural networks for real-world applications

Jun Tang received the M.E. degree from Nagoya University, Nagoya, Japan in 1988 and continued his doctoral studies there from 1988 to 1990. His research interests include span signal processing, artificial intelligence (including neural networks and machine learning), and computer operating systems. Currently, he holds the position of Chairman and Chief Architect at Wicresoft Co., Ltd., USA. Additionally, he served as a Visiting Professor at several universities, including Beijing University and Dalian University of Technology

Weiping Ding (Senior Member, IEEE) received the Ph.D. degree in computer science from Nanjing University of Aeronautics and Astronautics in 2013. From 2014 to 2015, he was a Postdoctoral Researcher at the Brain Research Center, National Chiao Tung University, Hsinchu, China. In 2016, he was a Visiting Scholar at National University of Singapore, Singapore. From 2017 to 2018, he was a Visiting Professor at University of Technology Sydney, Australia. Now he is the Dean of the School of Artificial Intelligence and Computer Science, Nantong University. His research interests include deep neural networks, granular data mining, and multimodal machine learning. He ranked within the top 2% Ranking of Scientists in the World by Stanford University, USA (2020−2025). He has published over 400 articles, including over 200 IEEE Transactions papers. His twenty authored/co-authored papers have been selected as ESI Highly Cited Papers. He has co-authored five books. He has holds more than 50 approved invention patents, including three U.S. patents and one Australian patent. He serves as an Associate Editor/Area Editor/Editorial Board member of more than 10 international prestigious journals, such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Fuzzy Systems, IEEE/CAA Journal of Automatica Sinica, IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Artificial Intelligence, Information Fusion, Information Sciences, Neurocomputing, Applied Soft Computing, Engineering Applications of Artificial Intelligence, Swarm and Evolutionary Computation, etc. He was/is the Leading Guest Editor of Special Issues in several prestigious journals, including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Fuzzy Systems, Information Fusion, Information Sciences, etc. Now he is the Co-Editor-in-Chief of such three international journals as Journal of Artificial Intelligence and Systems, Journal of Artificial Intelligence Advances, and Sustainable Machine Intelligence Journal

Zhenyu Lei received the Ph.D. degree in science and engineering from the University of Toyama, Toyama, Japan, in 2023. He is currently an Assistant Professor with the Faculty of Engineering, University of Toyama, Japan. His current research interests include evolutionary computation, machine learning, and neural network for real-world applications and optimization problems

Shangce Gao (Senior Member, IEEE) received the Ph.D. degree in innovative life science from University of Toyama, Toyama, Japan in 2011. He is currently a Professor with the Faculty of Engineering, University of Toyama, Japan. His current research interests include nature-inspired technologies, machine learning, and neural networks for real-world applications. He serves as an Associate Editor for many international journals such as IEEE Transactions on Neural Networks and Learning Systems, and IEEE/CAA Journal of Automatica Sinica
Corresponding author: Shangce Gao, e-mail: ding.wp@ntu.edu.cn
Jiaru Yang and Jun Tang contributed equally to this work.
Received Date: 2024-12-17
Revised Date: 2025-01-27
Accepted Date: 2025-02-22

Available Online: 2025-11-21

Abstract

Abstract

The modeling and optimization of wind farm layouts can effectively reduce the wake effect between turbine units, thereby enhancing the expected output power and avoiding negative influence. Traditional wind farm optimization often uses idealized wake models, neglecting the influence of wind shear at different elevations, which leads to a lack of precision in estimating wake effects and fails to meet the accuracy and reliability requirements of practical engineering. To address this, we have constructed a three-dimensional 3D wind farm optimization model that incorporates elevation, utilizing a 3D wake model to better reflect real-world conditions. We aim to assess the optimization state of the algorithm and provide strong incentives at the right moments to ensure continuous evolution of the population. To this end, we propose an evolutionary adaptation degree-guided genetic algorithm based on power-law perturbation (PPGA) to adapt multidimensional conditions. We select the offshore wind power project in Nantong, Jiangsu, China, as a study example and compare PPGA with other well-performing algorithms under this practical project. Based on the actual wind condition data, the experimental results demonstrate that PPGA can effectively tackle this complex problem and achieve the best power efficiency.
- 3D wake model,
- China’s southeastern coast,
- meta-heuristic,
- offshore wind farm,
- power-law perturbation-based genetic algorithm (PPGA)

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Jiaru Yang and Jun Tang contributed equally to this work.

References(48)

References

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Advanced 3D Wind Farm Layout Optimization Framework via Power-Law Perturbation-Based Genetic Algorithm

doi: 10.1109/JAS.2025.125351

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