A Pareto Front Based Evolutionary Model for Airfoil Self-Noise Prediction

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@InProceedings{Tahmassebi:2018:CECa,

• author = "Amirhessam Tahmassebi and Amir H. Gandomi and Anke Meyer-Baese",
• title = "A Pareto Front Based Evolutionary Model for Airfoil Self-Noise Prediction",
• booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2018",
• editor = "Marley Vellasco",
• address = "Rio de Janeiro, Brazil",
• month = "8-13 " # jul,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, Multi-Objective",
• URL = "https://ieeexplore.ieee.org/abstract/document/8477987",
• DOI = "doi:10.1109/CEC.2018.8477987",
• abstract = "According to NASA's report on the technologies that could reduce external aircraft noise by 10 dB, a challenge equally as important as finding approaches on airframe noise reduction is the demand to bring up strategies by which airframe noise can be predicted both accurately and rapidly. One of the components of the overall airframe noise is the self-noise of the airfoil itself. In this paper, an evolutionary symbolic implementation for airfoil self-noise prediction was proposed. Multi-objective genetic programming as a subset of evolutionary computation along with adaptive regression by mixing algorithm was used to create an executable fused model. The developed model was tested on the airfoil self-noise database and the performance of the developed model was compared to the previous works and benchmark machine learning algorithms. The reasonable results suggest that the proposed model can be applied to noise generation by low-Mach-number turbulent flows in aerospace, automotive, underwater, and wind turbine acoustic communities.",
• notes = "WCCI2018",

}

Genetic Programming entries for Amirhessam Tahmassebi A H Gandomi Anke Meyer-Baese

Citations