期刊
MATHEMATICS
卷 9, 期 17, 页码 -出版社
MDPI
DOI: 10.3390/math9172137
关键词
worst-case scenario; robust; min-max optimization; evolutionary algorithms
类别
资金
- European Commission's H2020 program under the Marie Sklodowska-Curie grant [722734]
- Slovenian Research Agency [P2-0098]
- Marie Curie Actions (MSCA) [722734] Funding Source: Marie Curie Actions (MSCA)
The proposed method in this work utilizes a differential evolution with an estimation of distribution algorithm to optimize worst-case scenario problems, reducing computational costs and improving efficiency.
Worst-case scenario optimization deals with the minimization of the maximum output in all scenarios of a problem, and it is usually formulated as a min-max problem. Employing nested evolutionary algorithms to solve the problem requires numerous function evaluations. This work proposes a differential evolution with an estimation of distribution algorithm. The algorithm has a nested form, where a differential evolution is applied for both the design and scenario space optimization. To reduce the computational cost, we estimate the distribution of the best worst solution for the best solutions found so far. The probabilistic model is used to sample part of the initial population of the scenario space differential evolution, using a priori knowledge of the previous generations. The method is compared with a state-of-the-art algorithm on both benchmark problems and an engineering application, and the related results are reported.
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