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Splitting for multi-objective optimization. (English) Zbl 1403.90604

Summary: We introduce a new multi-objective optimization (MOO) methodology based the splitting technique for rare-event simulation. The method generalizes the elite set selection of the traditional splitting framework, and uses both local and global sampling to sample in the decision space. In addition, an \(\varepsilon\)-dominance method is employed to maintain good solutions. The algorithm was compared with state-of-the art MOO algorithms using a prevailing set of benchmark problems. Numerical experiments demonstrate that the new algorithm is competitive with the well-established MOO algorithms and that it can outperform the best of them in various cases.

MSC:

90C29 Multi-objective and goal programming
68W20 Randomized algorithms

Software:

MOEA/D; PAES; GDE3; MOPSO
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Full Text: DOI

References:

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