New heuristics for multi-objective worst-case optimization in evidence-based robust design

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Abstract

This paper presents a non-nested algorithm for the solution of multi-objective min-max problems (MOMMP) in worst-case optimization. The algorithm has been devised for evidence-based robust optimization, where the lack of a defined probabilistic behaviour of the uncertain parameters makes it impossible to apply sample-based techniques and forces the designer to identify the worst case over the subdomains of the uncertainty space. In evidence theory, the robustness of the solutions is measured in terms of the Belief in the realization of the value of the design budgets, which acts as a lower bound to the unknown cumulative distribution function of the budget. Thus a means of finding robust solutions in preliminary design consists on applying the minimax model, where the worst-case budget over the uncertainty space is optimized over the control space. The paper proposes a novel heuristic to solve MOMMP and demonstrates its capability to approximate the worst-case Pareto front at a very reduced cost with respect to approaches based on nested optimization.

Original languageEnglish
Title of host publication2017 IEEE Congress on Evolutionary Computation (CEC)
Place of PublicationPiscataway, N.J.
Pages1519-1526
Number of pages8
DOIs
Publication statusPublished - 5 Jul 2017
Event2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Donostia-San Sebastian, Spain
Duration: 5 Jun 20178 Jun 2017

Conference

Conference2017 IEEE Congress on Evolutionary Computation, CEC 2017
CountrySpain
CityDonostia-San Sebastian
Period5/06/178/06/17

Keywords

  • minimization
  • cost reduction
  • optimization

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