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- W4361229364 abstract "In this study, we consider a continuous min--max optimization problem $min_{x in mathbb{X} max_{y in mathbb{Y}}}f(x,y)$ whose objective function is a black-box. We propose a novel approach to minimize the worst-case objective function $F(x) = max_{y} f(x,y)$ directly using a covariance matrix adaptation evolution strategy (CMA-ES) in which the rankings of solution candidates are approximated by our proposed worst-case ranking approximation (WRA) mechanism. We develop two variants of WRA combined with CMA-ES and approximate gradient ascent as numerical solvers for the inner maximization problem. Numerical experiments show that our proposed approach outperforms several existing approaches when the objective function is a smooth strongly convex--concave function and the interaction between $x$ and $y$ is strong. We investigate the advantages of the proposed approach for problems where the objective function is not limited to smooth strongly convex--concave functions. The effectiveness of the proposed approach is demonstrated in the robust berthing control problem with uncertainty.ngly convex--concave functions. The effectiveness of the proposed approach is demonstrated in the robust berthing control problem with uncertainty." @default.
- W4361229364 created "2023-03-31" @default.
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- W4361229364 date "2023-03-28" @default.
- W4361229364 modified "2023-09-27" @default.
- W4361229364 title "Covariance Matrix Adaptation Evolutionary Strategy with Worst-Case Ranking Approximation for Min--Max Optimization and its Application to Berthing Control Tasks" @default.
- W4361229364 doi "https://doi.org/10.48550/arxiv.2303.16079" @default.
- W4361229364 hasPublicationYear "2023" @default.
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