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.
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Acknowledgements
This work was supported by the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers, under grant number CE140100049. Qibin Duan would also like to acknowledge the support from the University of Queensland through the UQ International Scholarships scheme.
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Duan, Q., Kroese, D.P. Splitting for Multi-objective Optimization. Methodol Comput Appl Probab 20, 517–533 (2018). https://doi.org/10.1007/s11009-017-9572-5
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DOI: https://doi.org/10.1007/s11009-017-9572-5
Keywords
- Splitting method
- Multi-objective optimization
- Pareto front
- Pareto set
- Benchmarking
- Inverted generational distance