Abstract
In this paper we present a hybrid intelligent system for the hydrodynamic design of control surfaces on ships. Our main contribution here is the hybridization of Multiobjective Evolutionary Algorithms (MOEA) and a neural correction procedure in the fitness evaluation stage that permits obtaining solutions that are precise enough for the MOEA to operate with, while drastically reducing the computational cost of the simulation stage for each individual. The MOEA searches for the optimal solutions and the neuronal system corrects the deviations of the simplified simulation model to obtain a more realistic design. This way, we can exploit the benefits of a MOEA decreasing the computational cost in the evaluation of the candidate solutions while preesrving the reliability of the simulation model. The proposed hybrid system is successfully applied in the design of a 2D control surface for ships and extended to a 3D one.
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References
Coello, C., Van Veldhuizen, D., Lamont, G.: Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edn. Kluwer Academic Publishers, Dordrecht (2007)
Coello, C., Lamont, G.: Applications of Multi-Objective Evolutionary Algorithms. World Scientific, Singapore (2004)
Huang, V.L., Qin, A.K., Deb, K., Zitzler, E., Suganthan, P.N., Liang, J.J., Preuss, M., Huband, S.: Problem Definitions for Performance Assessment of Multi-objective Optimization Algorithms Special Session on Constrained Real-Parameter Optimization, Technical Report, Nanyang Technological University, Singapore (2007)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multi-Objective Genetic Algorithm. NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2000)
Suganthan, P.N.: Special Session on Constrained Real-Parameter Optimization, CEC (2007), http://www.ntu.edu.sg/home/EPNSugan/index_files/CEC-07/CEC-07-Comparison.pdf
Vinici, A., Quagliarella, D.: Inverse and Direct Airfoil Design using a Multiobjective Genetic Algorithm. AIA A Journal 35(9), 1499–1505 (1997)
Thomas, M.W.: Multi-Species Pareto Frontiers in Preliminary Submarine Design. Foundations of Computing and Decision Sciences 25(4), 273–289 (2000)
Mierzwicki, T.S.: Risk Index for Multi-Objective Design Optimization of Naval Ships MSc thesis, Virginia Polytechnic Institute and State University, (2003)
Olcer, A.I.: A hybrid approach for multi-objective combinatorial optimisation problems in ship design and shipping. Computers&Operations Research 35(9), 2760–2775 (2008)
Clemente Fernández, J.A., Pérez Rojas, L., Pérez Arribas, F.: Optimización de una forma hidrodinámica mediante algoritmos evolutivos XIX Congreso Panamericano de IngenierPía Naval, Transporte Marítimo e Ingeniería Portuaria (2005)
Thwaites, B.: On the momentum equation in laminar boundary layer flow. ARC RM 2587 (1952)
Cebeci, T., Smith, A.: Analysis of turbulent Boundary layers, pp. 332–333 (1974)
Head, M.R.: Entrainment in the turbulent boundary layers. ARC R&M 3152 (1958)
Martin, O.L., Hansen: Aerodynamics of Wind, pp. 11–66. Turbines Science Publishers. Ltd. (2000)
Moores, J.: Potential Flow 2-Dimensional Vortex Panel Model: Applications to Wingmills, University of Toronto (2003)
Abbot, H., Von Doenhoff, A.E.: Theory of Wing Sections. Dover, New York (1949)
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Díaz-Casás, V., Bellas, F., López-Peña, F., Duro, R. (2009). Hydrodynamic Design of Control Surfaces for Ships Using a MOEA with Neuronal Correction. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_12
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DOI: https://doi.org/10.1007/978-3-642-02319-4_12
Publisher Name: Springer, Berlin, Heidelberg
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