Hydrodynamic Design of Control Surfaces for Ships Using a MOEA with Neuronal Correction

Authors:
V. Díaz-Casás;Francisco Bellas;Fernando López-Peña;Richard Duro
Affiliations:
Integrated Group for Engineering Research, Universidade da Coruña, Ferrol, Spain 15403;Integrated Group for Engineering Research, Universidade da Coruña, Ferrol, Spain 15403;Integrated Group for Engineering Research, Universidade da Coruña, Ferrol, Spain 15403;Integrated Group for Engineering Research, Universidade da Coruña, Ferrol, Spain 15403
Venue:
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Year:
2009

Citing 3
Cited 1

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A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation

Evolutionary algorithm characterization in real parameter optimization problems

Applied Soft Computing

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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.