A memetic approach to the solution of constrained min-max problems

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Abstract

This paper proposes a novel memetic algorithm for the solution of constrained min-max problems that derive from the optimal design of complex systems under worst-case conditions. In this context the maximisation of a quantity of interest over the space of uncertain variables is required to identify the worst-case scenario (or worst-case solution under uncertainty). An optimal design vector is then identified such that the worst-case value of the quantity of interest is minimised. In the most general case, both maximisation and minimisation are subject to strict feasibility constraints. The ultimate goal of the minimisation problem is to identify the design solution that is feasible for all possible values of the uncertain parameters.
Original languageEnglish
Number of pages8
Publication statusPublished - 13 Jun 2019
Event2019 IEEE Congress on Evolutionary Computation - Wellington, New Zealand
Duration: 10 Jun 201913 Jun 2019

Conference

Conference2019 IEEE Congress on Evolutionary Computation
Abbreviated titleIEEE CEC 2019
CountryNew Zealand
CityWellington
Period10/06/1913/06/19

Keywords

  • worst case scenario
  • min-max
  • epistemic uncertainty
  • benchmark

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    Cite this

    Filippi, G., & Vasile, M. (2019). A memetic approach to the solution of constrained min-max problems. Paper presented at 2019 IEEE Congress on Evolutionary Computation, Wellington, New Zealand.