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Communication Dans Un Congrès Année : 2016

HBA-1: A Hybrid Bi-Objective Optimizer for Black-Box Problems

Résumé

This paper introduces a new bi-objective optimization approach for treating black-box models. Black-box models coming from the industry are often complex and some cannot always be treated with techniques that are solely deterministic. To overcome this issue whilst avoiding opting for a stochastic solution that would result in high computational costs, a combination of mono-objective solvers under a reference-point based scheme is proposed. Validation is performed against a nonlinearly constrained black-box model of a slotless permanent-magnet actuator. Satisfactory results reflect the effectiveness and utility of the optimizer.
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Dates et versions

hal-01400968 , version 1 (22-11-2016)

Identifiants

  • HAL Id : hal-01400968 , version 1

Citer

Kaveh Babanezhad, Jean Bigeon. HBA-1: A Hybrid Bi-Objective Optimizer for Black-Box Problems. Proc. of CEFC2016, IEEE, Nov 2016, Miami, United States. pp.5090. ⟨hal-01400968⟩
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