A Method for Constrained Multiobjective Optimization Based on SQP Techniques
A Method for Constrained Multiobjective Optimization Based on SQP Techniques
We propose a method for constrained and unconstrained nonlinear multiobjective optimization problems that is based on an SQP-type approach. The proposed algorithm maintains a list of nondominated points that is improved both for spread along the Pareto front and optimality by solving single-objective constrained optimization problems. These single-objective problems are derived as SQP problems based on the given nondominated points.
Under appropriate differentiability assumptions we discuss convergence to local optimal Pareto points. We provide numerical results for a set of unconstrained and constrained multiobjective optimization problems in the form of performance and data profiles, where several performance metrics are used. The numerical results confirm the superiority of the proposed algorithm against a state-of-the-art multiobjective solver and a classical scalarization approach, both in the quality of the approximated Pareto front and in the computational effort necessary to compute the approximation.
2091-2119
Fliege, Joerg
54978787-a271-4f70-8494-3c701c893d98
Vaz, Ismael F.
b612c114-7818-4a1e-b988-6ac83bdbf010
Fliege, Joerg
54978787-a271-4f70-8494-3c701c893d98
Vaz, Ismael F.
b612c114-7818-4a1e-b988-6ac83bdbf010
Fliege, Joerg and Vaz, Ismael F.
(2016)
A Method for Constrained Multiobjective Optimization Based on SQP Techniques.
SIAM Journal on Optimization, 26 (4), .
(doi:10.1137/15M1016424).
Abstract
We propose a method for constrained and unconstrained nonlinear multiobjective optimization problems that is based on an SQP-type approach. The proposed algorithm maintains a list of nondominated points that is improved both for spread along the Pareto front and optimality by solving single-objective constrained optimization problems. These single-objective problems are derived as SQP problems based on the given nondominated points.
Under appropriate differentiability assumptions we discuss convergence to local optimal Pareto points. We provide numerical results for a set of unconstrained and constrained multiobjective optimization problems in the form of performance and data profiles, where several performance metrics are used. The numerical results confirm the superiority of the proposed algorithm against a state-of-the-art multiobjective solver and a classical scalarization approach, both in the quality of the approximated Pareto front and in the computational effort necessary to compute the approximation.
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M101642.pdf
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Accepted/In Press date: 16 August 2016
e-pub ahead of print date: 17 October 2016
Organisations:
Operational Research
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Local EPrints ID: 395345
URI: http://eprints.soton.ac.uk/id/eprint/395345
ISSN: 1052-6234
PURE UUID: 9de90ce8-d5c5-48f7-98e2-c832f05789f8
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Date deposited: 27 May 2016 12:57
Last modified: 15 Mar 2024 05:36
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Author:
Ismael F. Vaz
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