1 research outputs found
Covariance Matrix Adaptation Evolutionary Strategy with Worst-Case Ranking Approximation for Min--Max Optimization and its Application to Berthing Control Tasks
In this study, we consider a continuous min--max optimization problem
whose objective
function is a black-box. We propose a novel approach to minimize the worst-case
objective function 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 and 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