Hybridization of a Multi-objective Genetic Algorithm, a Neural Network and a Classical Optimizer for a Complex Design Problem in Fluid Dynamics

Abstract

This paper describes the combination of several optimization technologies that can be used to tackle challenging design problems. The approach, that uses a multi-objective genetic algorithm, a neural network, and a gradient-based optimizer, is first outlined with the help of a computationally inexpensive mathematical test function. Then the methodology is applied to the design of a sailing yacht fin keel, coupling the optimization codes to 3D Navier-Stokes simulations. To perform the multi-objective optimization task a parallel computer is employed.