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

Simone Alicino, Massimiliano Vasile

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

5 Citations (Scopus)
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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.


Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
Pages1179-1186
Number of pages8
DOIs
Publication statusPublished - 6 Jul 2014
Event2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing, United Kingdom
Duration: 6 Jul 201411 Jul 2014

Conference

Conference2014 IEEE Congress on Evolutionary Computation, CEC 2014
CountryUnited Kingdom
CityBeijing
Period6/07/1411/07/14

Keywords

  • evolutionary computation
  • statistical analysis
  • epistemic uncertainty
  • evolutionary approach
  • computational cost reduction technique

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    Alicino, S., & Vasile, M. (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 (pp. 1179-1186) https://doi.org/10.1109/CEC.2014.6900286