Multiple-gradient Descent Algorithm for Pareto-Front Identification

DOI10.1007/978-94-017-9054-3_3zbMath1320.65093OpenAlexW407273095MaRDI QIDQ5259697

Jean-Antoine Désidéri

Publication date: 29 June 2015

Published in: Computational Methods in Applied Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-94-017-9054-3_3

zbMATH Keywords

multi-objective optimizationconvex hulldescent directionGram-Schmidt orthogonalization processBFGS quasi-Newton method

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05)

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Cites Work

• Multiple-gradient descent algorithm (MGDA) for multiobjective optimization
• The normalized normal constraint method for generating the Pareto frontier
• Constructing a Pareto front approximation for decision making
• PAINT: Pareto front interpolation for nonlinear multiobjective optimization
• Cooperation and competition in multidisciplinary optimization
• Nonlinear multiobjective optimization
• Steepest descent methods for multicriteria optimization.
• A steepest descent method for vector optimization
• Comparison Between Two Multi-Objective Optimization Algorithms: PAES and MGDA. Testing MGDA on Kriging Metamodels
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