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Empirical Performance of the Approximation of the Least Hypervolume Contributor
Krzysztof Nowak1, Marcus Märtens2, and Dario Izzo1
1European Space Agency, Noordwijk, The Netherlands
2TU Delft, Delft, The Netherlands
Abstract. A fast computation of the hypervolume has become a crucial component for the quality assessment and the performance of modern multi-objective evolutionary optimization algorithms. Albeit recent improvements, exact computation becomes quickly infeasible if the optimization problems scale in their number of objectives or size. To overcome this issue, we investigate the potential of using approximation instead of exact computation by benchmarking the state of the art hypervolume algorithms for different geometries, dimensionality and number of points. Our experiments outline the threshold at which exact computation starts to become infeasible, but approximation still applies, highlighting the major factors that influence its performance.
Keywords: Hypervolume indicator, performance indicators, multi- objective optimization, many-objective optimization, approximation algorithms
LNCS 8672, p. 662 ff.
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© Springer International Publishing Switzerland 2014