Splitting for Multi-objective Optimization

Qibin Duan () and Dirk P. Kroese ()
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Qibin Duan: The University of Queensland
Dirk P. Kroese: The University of Queensland

Methodology and Computing in Applied Probability, 2018, vol. 20, issue 2, 517-533

Abstract: Abstract 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 𝜖-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.

Keywords: Splitting method; Multi-objective optimization; Pareto front; Pareto set; Benchmarking; Inverted generational distance; 68W20; 90C29 (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s11009-017-9572-5

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