The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Statistical Improvement Criteria for Use in Multiobjective Design Optimization
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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Cites
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
Efficient Global Optimization of Expensive Black-Box Functions
Jones, D. R., Schonlau, M., & Welch, W. J. (1998) · Journal of Global Optimization · doi:10.1023/a:1008306431147 · Cited by 8,070
Cited by
Expensive Multiobjective Optimization by MOEA/D With Gaussian Process Model
Zhang, Q., Liu, W., Tsang, E., & Virginas, B. (2010) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2009.2033671 · Cited by 746
Nandy, A., Duan, C., Taylor, M. G., Liu, F., Steeves, A. H., & Kulik, H. J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00347 · Cited by 358
Janet, J. P., Ramesh, S., Duan, C., & Kulik, H. J. (2020) · ACS Central Science · doi:10.1021/acscentsci.0c00026 · Cited by 200
Metadata
- Authors
- Keane, A. J.
- Venue
- AIAA Journal
- Published
- 2006-04
- Volume
- 44
- Issue
- 4
- Pages
- 879-891
- ISSN
- 0001-1452 (ISSN-L)
- 1533-385X
- OpenAlex
- W2098629435
- Cited by
- 312
- References
- 2
- FWCI
- 11.28
- Type
- journal article
- Language
- en
- Open access
- closed
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