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An EMO algorithm using the hypervolume measure as selection criterion. (English) Zbl 1109.68595
Coello Coello, Carlos A. (ed.) et al., Evolutionary multi-criterion optimization. Third international conference, EMO 2005, Guanajuato, Mexico, March 9–11, 2005. Proceedings. Berlin: Springer (ISBN 3-540-24983-4/pbk). Lecture Notes in Computer Science 3410, 62-76 (2005).
Summary: The hypervolume measure is one of the most frequently applied measures for comparing the results of evolutionary multiobjective optimization algorithms (EMOA). The idea to use this measure for selection is self-evident. A steady-state EMOA will be devised, that combines concepts of non-dominated sorting with a selection operator based on the hypervolume measure. The algorithm computes a well distributed set of solutions with bounded size thereby focussing on interesting regions of the Pareto front(s). By means of standard benchmark problems the algorithm will be compared to other well established EMOA. The results show that our new algorithm achieves good convergence to the Pareto front and outperforms standard methods in the hypervolume covered.
We also studied the applicability of the new approach in the important field of design optimization. In order to reduce the number of time consuming precise function evaluations, the algorithm will be supported by approximate function evaluations based on Kriging metamodels. First results on an airfoil redesign problem indicate a good performance of this approach, especially if the computation of a small, bounded number of well-distributed solutions is desired.
For the entire collection see [Zbl 1069.68002].

68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
90C29 Multi-objective and goal programming
90C59 Approximation methods and heuristics in mathematical programming
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