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

Ortega, C. and Vasile, M.; (2017) New heuristics for multi-objective worst-case optimization in evidence-based robust design. In: 2017 IEEE Congress on Evolutionary Computation (CEC). Institute of Electrical and Electronics Engineers Inc., ESP, pp. 1519-1526. ISBN 9781509046010 (https://doi.org/10.1109/CEC.2017.7969483)

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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.

  • Item type:Book Section
    ID code:62811
    Dates:
    Date
    Event
    5 July 2017
    Published
    7 March 2017
    Accepted
    Notes:(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Mechanical engineering and machinery
    Department:Faculty of Engineering > Mechanical and Aerospace Engineering
    Depositing user: Pure Administrator
    Date deposited:10 Jan 2018 14:27
    Last modified:11 Apr 2024 00:11
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/62811
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