An evolutionary approach to the solution of multi-objective min-max problems in evidence-based robust optimization

Tools

Alicino, Simone and Vasile, Massimiliano; (2014) An evolutionary approach to the solution of multi-objective min-max problems in evidence-based robust optimization. In: Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. Institute of Electrical and Electronics Engineers Inc., GBR, pp. 1179-1186. ISBN 9781479914883 (https://doi.org/10.1109/CEC.2014.6900286)

[thumbnail of Alicino-Vasile-CEC-2014-multi-objective-min-max-problems-in-evidence-based-robust-optimization]
Preview
 Text. Filename: Alicino_Vasile_CEC_2014_multi_objective_min_max_problems_in_evidence_based_robust_optimization.pdf
Accepted Author Manuscript
Download (929kB)| Preview

Abstract

This paper presents an evolutionary approach to solve the multi-objective min-max problem (MOMMP) that derives from the maximization of the Belief in robust design optimization. In evidence-based robust optimization, the solutions that minimize the design budgets are robust under epistemic uncertainty if they maximize the Belief in the realization of the value of the design budgets. Thus robust solutions are found by minimizing, with respect to the design variables, the global maximum with respect to the uncertain variables. This paper presents an algorithm to solve MOMMP, and a computational cost reduction technique based on Kriging metamodels. The results show that the algorithm is able to accurately approximate the Pareto front for a MOMMP at a fraction of the computational cost of an exact calculation.

ORCID iDs

Alicino, Simone ORCID logoORCID: https://orcid.org/0000-0002-0213-7719 and Vasile, Massimiliano ORCID logoORCID: https://orcid.org/0000-0001-8302-6465;
CORE (COnnecting REpositories)