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Normal-Boundary Intersection: A New Method for Generating the Pareto Surface in Nonlinear Multicriteria Optimization Problems
Abstract
This paper proposes an alternate method for finding several Pareto optimal points for a general nonlinear multicriteria optimization problem. Such points collectively capture the trade-off among the various conflicting objectives. It is proved that this method is independent of the relative scales of the functions and is successful in producing an evenly distributed set of points in the Pareto set given an evenly distributed set of parameters, a property which the popular method of minimizing weighted combinations of objective functions lacks. Further, this method can handle more than two objectives while retaining the computational efficiency of continuation-type algorithms. This is an improvement over continuation techniques for tracing the trade-off curve since continuation strategies cannot easily be extended to handle more than two objectives.
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Cited by
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Zhang, Q., & Li, H. (2007) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2007.892759 · Cited by 10,069
Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories
Häse, F., Roch, L. M., & Aspuru-Guzik, A. (2018) · Chemical Science · doi:10.1039/c8sc02239a · Cited by 161
MOGOA algorithm for constrained and unconstrained multi-objective optimization problems
Tharwat, A., Houssein, E. H., Ahmed, M. M., Hassanien, A. E., & Gabel, T. (2018) · Applied Intelligence · doi:10.1007/s10489-017-1074-1 · Cited by 114
Metadata
- Authors
- Das, I., & Dennis, J. E.
- Venue
- SIAM Journal on Optimization
- Published
- 1998-08
- Volume
- 8
- Issue
- 3
- Pages
- 631-657
- ISSN
- 1052-6234 (ISSN-L)
- 1095-7189
- OpenAlex
- W2024008934
- Cited by
- 2,654
- References
- 0
- FWCI
- 11.55
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
