A surrogate-assisted evolutionary algorithm with an adaptive sample selection strategy for wind farm layout optimization

References

• Asaah, P., L. L. Hao, and J. Ji. 2021. Optimal placement of wind turbines in wind farm layout using particle swarm optimization. Journal of Modern Power Systems and Clean Energy 9 (2):367–75. doi:10.35833/MPCE.2019.000087.
   Web of Science ®Google Scholar
• Awad, N. H., M. Z. Ali, R. Mallipeddi, and P. N. Suganthan. 2018. An improved differential evolution algorithm using efficient adapted surrogate model for numerical optimization. Information Sciences 451:326–47. doi:10.1016/j.ins.2018.04.024.
   Web of Science ®Google Scholar
• Díaz-Manríquez, A., G. Toscano, and C. A. Coello-Coello. 2017. Comparison of metamodeling techniques in evolutionary algorithms. Soft Computing 21 (19):5647–63. doi:10.1007/s00500-016-2140-z.
   Web of Science ®Google Scholar
• Elsayed, S. M., T. Ray, and R. A. Sarker. 2014. A surrogate-assisted differential evolution algorithm with dynamic parameters selection for solving expensive optimization problems. IEEE Transactions on Evolutionary Computation 2014:1062–68. doi:10.1109/CEC.2014.6900351.
   Google Scholar
• Emami, A., and P. Noghreh. 2010. New approach on optimization in placement of wind turbines within wind farm by genetic algorithms. Renewable Energy 35 (7):1559–64. doi:10.1016/j.renene.2009.11.026.
   Web of Science ®Google Scholar
• Fan, C. D., B. Hou, J. H. Zheng, L. Y. Xiao, and L. Z. Yi. 2020. A surrogate-assisted particle swarm optimization using ensemble learning for expensive problems with small sample datasets. Applied Soft Computing 91:106242. doi:10.1016/j.asoc.2020.106242.
   Web of Science ®Google Scholar
• Fleming, P. A., P. M. Gebraad, S. Lee, J.-W. van Wingerden, K. Johnson, M. Churchfield, J. Michalakes, P. Spalart, and P. Moriarty. 2014. Evaluating techniques for redirecting turbine wakes using SOWFA. Renewable Energy 70:211–18. doi:10.1016/j.renene.2014.02.015.
   Web of Science ®Google Scholar
• Fu, C. B., P. Wang, L. Zhao, and X. J. Wang. 2020. A distance correlation-based Kriging modeling method for high-dimensional problems. Knowledge-Based Systems 206:106356. doi:10.1016/j.knosys.2020.106356.
   Web of Science ®Google Scholar
• Gao, K. F., G. Mei, S. Cuomo, F. Piccialli, and N. X. Xu. 2020. ARBF: Adaptive radial basis function interpolation algorithm for irregularly scattered point sets. Soft Computing 24 (23):17693–704. doi:10.1007/s00500-020-05211-0.
   Web of Science ®Google Scholar
• Goel, T., R. T. Haftka, W. Shyy, and N. V. Queipo. 2007. Ensemble of surrogates. Structural and Multidisciplinary Optimization 33 (3):199–216. doi:10.1007/s00158-006-0051-9.
   Web of Science ®Google Scholar
• Grady, S., M. Hussaini, and M. Abdullah. 2005. Placement of wind turbines using genetic algorithms. Renewable Energy 30 (2):259–70. doi:10.1016/j.renene.2004.05.007.
   Web of Science ®Google Scholar
• GWEC. 2019. Global Wind Report. https://gwec.net/global-wind-report-2019.
   Google Scholar
• Herbert-Acero, J., O. Probst, P. E. Rethore, G. Larsen, and K. Castillo-Villar. 2014. A review of methodological approaches for the design and optimization of wind farms. Energies 7 (11):6930–7016. doi:10.3390/en7116930.
   Web of Science ®Google Scholar
• Huang, C. W., R. Bouchaïb, E. H. Abdelkhalak, and H. Bai. 2018. CMA evolution strategy assisted by Kriging model and approximate ranking. Applied Intelligence 48 (11):4288–304. doi:10.1007/s10489-018-1193-3.
   Web of Science ®Google Scholar
• Jin, Y. C., H. D. Wang, T. Chugh, D. Guo, and K. Miettinen. 2019. Data-driven evolutionary optimization: An overview and case studies. IEEE Transactions on Evolutionary Computation 23 (3):442–58. doi:10.1109/TEVC.2018.2869001.
   Web of Science ®Google Scholar
• Li, E. Y. 2019. An adaptive surrogate assisted differential evolutionary algorithm for high dimensional constrained problems. Applied Soft Computing 85:105752. doi:10.1016/j.asoc.2019.105752.
   Web of Science ®Google Scholar
• Liao, P., C. L. Sun, G. C. Zhang, and Y. C. Jin. 2020. Multi-surrogate multi-tasking optimization of expensive problems. Knowledge-Based Systems 205:106262. doi:10.1016/j.knosys.2020.106262.
   Web of Science ®Google Scholar
• Li, F., X. W. Cai, and L. Gao. 2019. Ensemble of surrogates assisted particle swarm optimization of medium scale expensive problems. Applied Soft Computing 74:291–305. doi:10.1016/j.asoc.2018.10.037.
   Web of Science ®Google Scholar
• Li, X. M., and S. J. Li. 2021. An adaptive surrogate-assisted particle swarm optimization for expensive problems. Soft Computing 25 (24):15051–65. doi:10.1007/s00500-021-06348-2.
   Web of Science ®Google Scholar
• Li, F., W. M. Shen, X. W. Cai, L. Gao, and G. G. Wang. 2020. A fast surrogate-assisted particle swarm optimization algorithm for computationally expensive problems. Applied Soft Computing 92:106303. doi:10.1016/j.asoc.2020.106303.
   Web of Science ®Google Scholar
• Liu, H. T., J. G. Meng, S. L. Xu, S. H. Yang, and X. F. Wang. 2016. Optimal weighted pointwise ensemble of radial basis functions with different basis functions. AIAA Journal 54 (10):3117–33. doi:10.2514/1.J054664.
   Web of Science ®Google Scholar
• Masato, K., and M. Hidetoshi. 2020. The limiting distribution of combining the t and Wilcoxon rank sum tests. Statistics 54 (4):871–84. doi:10.1080/02331888.2020.1809662.
   Web of Science ®Google Scholar
• Metropolis, N., A. Rosenbluth, M. Rosenbluth, A. H. Teller, and E. Teller. 1953. Simulated annealing. The Journal of Chemical Physics 21 (6):1087–92. doi:10.1111/j.1467-9574.1989.tb01245.x.
   Google Scholar
• Mosetti, G., C. Poloni, and B. Diviacco. 1994. Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm. Journal of Wind Engineering & Industrial Aerodynamics 51:105–16. doi:10.1016/0167-6105(94)90080-9.
   Web of Science ®Google Scholar
• Pan, J. S., N. X. Liu, S. C. Chu, and T. T. Lai. 2020. An efficient surrogate-assisted hybrid optimization algorithm for expensive optimization problems. Information Sciences 561:304–25. doi:10.1016/j.ins.2020.11.056.
   Web of Science ®Google Scholar
• Shakoor, R., M. Y. Hassan, A. Raheem, and Y. K. Wu. 2016. Wake effect modeling: A review of wind farm layout optimization using Jensen’s model. Renewable & Sustainable Energy Reviews 58:1048–59. doi:10.1016/j.rser.2015.12.229.
   Web of Science ®Google Scholar
• Song, Y., and I. Paek. 2020. Prediction and validation of the annual energy production of a wind turbine using WindSim and a dynamic wind turbine model. Energies 13:6604. doi:10.3390/en13246604.
   Web of Science ®Google Scholar
• Sousa-Ferreira, I., and D. Sousa. 2017. A review of velocity-type PSO variants. Journal of Algorithms & Computational Technology 11 (1):23–30. doi:10.1177/1748301816665021.
   Web of Science ®Google Scholar
• Sun, C. L., Y. C. Jin, R. Cheng, J. L. Ding, and J. C. Zeng. 2017. Surrogate-assisted cooperative swarm optimization of high-dimensional expensive problems. IEEE Transactions on Evolutionary Computation 21 (4):644–60. doi:10.1109/TEVC.2017.2675628.
   Web of Science ®Google Scholar
• Sun, C. L., Y. C. Jin, J. C. Zeng, and Y. Yu. 2015. A two-layer surrogate-assisted particle swarm optimization algorithm. Soft Computing 19 (6):1461–75. doi:10.1007/s00500-014-1283-z.
   Web of Science ®Google Scholar
• Tao, S. Y., Q. S. Xu, and A. Feijóo. 2020. Nonuniform wind farm layout optimization: A state-of-the-art review. Energy 209:118339. doi:10.1016/j.energy.2020.118339.
   Web of Science ®Google Scholar
• Tao, S. Y., Q. S. Xu, A. Feijóo, G. Zheng, J. M. Zhou. 2020. Wind farm layout optimization with a three-dimensional Gaussian wake model. Renewable Energy 159:553–69. doi:10.1016/j.renene.2020.06.003.
   Web of Science ®Google Scholar
• Wang, H. D., Y. C. Jin, and J. Doherty. 2017. Committee-based active learning for surrogate-assisted particle swarm optimization of expensive problems. IEEE Transactions on Cybernetics 47 (9):2664–77. doi:10.1109/TCYB.2017.2710978.
   PubMed Web of Science ®Google Scholar
• Wu, J. L., Z. Luo, N. Zhang, and W. Gao. 2018. A new sequential sampling method for constructing the high-order polynomial surrogate models. Engineering Computations 35 (2):529–64. doi:10.1108/EC-05-2016-0160.
   Web of Science ®Google Scholar
• Yondo, R., E. Andrés, and E. Valero. 2018. A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses. Progress in Aerospace Sciences 96:23–61. doi:10.1016/j.paerosci.2017.11.003.
   Web of Science ®Google Scholar
• Zhai, J. Y., and F. Boukouvala. 2019. Nonlinear variable selection algorithms for surrogate modeling. AIChE Journal 65 (8):e16601. doi:10.1002/aic.16601.
   Web of Science ®Google Scholar
• Zhen, Z., W. R. Zhao, and S. J. Li. 2021.Wind farm layout optimization based on 3D wake model and surrogate model. International Journal of Green Energy 19: 956–66. doi: 10.1080/15435075.2021.1976651.
   Web of Science ®Google Scholar