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Use of a Biobjective Direct Search Algorithm in the Process Design of Material Science Applications

Aimen E. Gheribi (aimen.gheribi***at***polymtl.ca)
Jean-Philippe Harvey (jean-philippe.harvey***at***mail.mcgill.ca)
Eve Belisle (e.belisle***at***uq.edu.au)
Christian Robelin (christian.robelin***at***polymtl.ca)
Patrice Chartrand (patrice.chartrand***at***polymtl.ca)
Arthur D. Pelton (arthur.pelton***at***polymtl.ca)
Christopher W. Bale (cbale***at***polymtl.ca)
Sebastien Le Digabel (Sebastien.Le.Digabel***at***gerad.ca)

Abstract: This work describes the application of a direct search method to the optimization of problems of real industrial interest, namely three new material science applications designed with the FactSage software. The search method is BiMADS, the biobjective version of the mesh adaptive direct search (MADS) algorithm, designed for blackbox optimization. We give a general description of the algorithm, and, for each of the three test cases, we describe the optimization problem, discuss the algorithmic choices, and give numerical results to demonstrate the efficiency of BiMADS for real cases of alloy and process design.

Keywords: Blackbox optimization, derivative-free optimization, biobjective optimization, mesh adaptive direct search, material science, alloys design, process design.

Category 1: Nonlinear Optimization

Category 2: Applications -- Science and Engineering (Chemical Engineering )


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Entry Submitted: 12/02/2014
Entry Accepted: 12/02/2014
Entry Last Modified: 08/03/2015

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