Inverse multi-objective robust evolutionary design |
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Authors: | Dudy Lim Yew-Soon Ong Yaochu Jin Bernhard Sendhoff Bu Sung Lee |
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Affiliation: | (1) School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore, 639798, Singapore;(2) Honda Research Institute Europe GmbH, Carl-Legien-Strasse 30, 63073 Offenbach, Germany |
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Abstract: | In this paper, we present an Inverse Multi-Objective Robust Evolutionary (IMORE) design methodology that handles the presence
of uncertainty without making assumptions about the uncertainty structure. We model the clustering of uncertain events in
families of nested sets using a multi-level optimization search. To reduce the high computational costs of the proposed methodology
we proposed schemes for (1) adapting the step-size in estimating the uncertainty, and (2) trimming down the number of calls
to the objective function in the nested search. Both offline and online adaptation strategies are considered in conjunction
with the IMORE design algorithm. Design of Experiments (DOE) approaches further reduce the number of objective function calls
in the online adaptive IMORE algorithm. Empirical studies conducted on a series of test functions having diverse complexities
show that the proposed algorithms converge to a set of Pareto-optimal design solutions with non-dominated nominal and robustness
performances efficiently.
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