Differential Evolution with Estimation of Distribution for Worst-Case Scenario Optimization

Margarita Antoniou and Gregor Papa
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Margarita Antoniou: Computer Systems Department, Jožef Stefan Institute, Jamova c. 39, SI-1000 Ljubljana, Slovenia
Gregor Papa: Computer Systems Department, Jožef Stefan Institute, Jamova c. 39, SI-1000 Ljubljana, Slovenia

Mathematics, 2021, vol. 9, issue 17, 1-22

Abstract: 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.

Keywords: worst-case scenario; robust; min-max optimization; evolutionary algorithms (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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