Genetic algorithms in multiobjective design optimisation

Optimisation is a challenge for computerized multidisciplinary design. With multidisciplinary design optmisation there are several objectives to optimise, originating from different disciplines. Therefore no obvious method to splice the multiple objectives into a single objective exists. Multiobjective optimisation is a method that can deal with multiple objectives. The genetic algorithm is a global optimisation method, which is inspired by natural evolution. It uses a population of solutions that evolves to better solutions with help of recombination and mutation. A genetic algorithm can cope with a design space that is, for example, discontinous or convex. That and the global character of the genetic algorithm make it a suitable algorithm to solve multiobjective optimisation problems. In this report an introduction to optimisation problems and genetic algorithms is given. Furthermore, the results are shown of two multiobjective optimisation problems: parameter choice in time integration methods and airfoil design.