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Multi-objective optimization using surrogate functions

: Hornung, Nils; Nikitina, Lialia; Clees, Tanja

Rodrigues, H. ; Associação Portuguesa de Mecânica Teórica, Aplicada e Computacional -APMTAC-:
EngOpt 2010, 2nd International Conference Engineering Optimization : Lisbon, 6-9 September 2010; Book of abstracts and CD-ROM proceedings
Lisboa: APMTAC, 2010
ISBN: 978-989-96264-3-0
International Conference Engineering Optimization (EngOpt) <2, 2010, Lisbon>
Fraunhofer SCAI ()
multi-objective optimization; surrogate models; linear programming; trust region methods

In multi-objective optimization problems, expensive high-fidelity simulations are commonly replaced by response surfaces approximating the parameter-criteria dependencies. Response surface or metamodel approximations allow for fast evaluations of a large set of designs. As it is known from the literature, the problem of finding the optima of a set of vectors can be implemented efficiently as a search strategy. Such an approach does not yield good results, though, if there is a strong bias within the function that disadvantages Pareto-optimal designs in objective space. As an answer we suggest a novel algorithm that produces a trajectory towards the Pareto front starting from an initial design. The applicability of our algorithm is limited to the case where no local Pareto fronts exist along the trajectory.