Statistical improvement criteria for use in multiobjective design optimisation
Statistical improvement criteria for use in multiobjective design optimisation
Design of experiment and response surface modeling methods are applied to the problem of constructing Pareto fronts for computationally expensive multiobjective design optimization problems. The work presented combines design of experiment methods with kriging (Gaussian process) models to enable the parallel evolution of multiobjective Pareto sets.
This is achieved via the use of updating schemes based on new extensions of the expected improvement criterion commonly applied in single-objective searches. The approaches described provide a statistically coherent means of solving expensive multiobjective design problems using single-objective search tools. They are compared to the use of nondominated sorting genetic algorithm (NSGA-ii) based multiobjective searches, both with and without response surface support. The new approaches are shown to give more exact, wider ranging, and more evenly populated Pareto fronts than the genetic algorithm based searches at reduced or similar cost.
879-891
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
2006
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Keane, A.J.
(2006)
Statistical improvement criteria for use in multiobjective design optimisation.
AIAA Journal, 44 (4), .
Abstract
Design of experiment and response surface modeling methods are applied to the problem of constructing Pareto fronts for computationally expensive multiobjective design optimization problems. The work presented combines design of experiment methods with kriging (Gaussian process) models to enable the parallel evolution of multiobjective Pareto sets.
This is achieved via the use of updating schemes based on new extensions of the expected improvement criterion commonly applied in single-objective searches. The approaches described provide a statistically coherent means of solving expensive multiobjective design problems using single-objective search tools. They are compared to the use of nondominated sorting genetic algorithm (NSGA-ii) based multiobjective searches, both with and without response surface support. The new approaches are shown to give more exact, wider ranging, and more evenly populated Pareto fronts than the genetic algorithm based searches at reduced or similar cost.
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Published date: 2006
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Local EPrints ID: 23536
URI: http://eprints.soton.ac.uk/id/eprint/23536
ISSN: 0001-1452
PURE UUID: 17e999cf-d073-4646-868a-180c88a31941
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Date deposited: 12 Apr 2006
Last modified: 16 Mar 2024 02:53
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