The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Performance assessment of multiobjective optimizers: an analysis and review
Abstract
An important issue in multiobjective optimization is the quantitative comparison of the performance of different algorithms. In the case of multiobjective evolutionary algorithms, the outcome is usually an approximation of the Pareto-optimal set, which is denoted as an approximation set, and therefore the question arises of how to evaluate the quality of approximation sets. Most popular are methods that assign each approximation set a vector of real numbers that reflect different aspects of the quality. Sometimes, pairs of approximation sets are also considered. In this study, we provide a rigorous analysis of the limitations underlying this type of quality assessment. To this end, a mathematical framework is developed which allows one to classify and discuss existing techniques.
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Citations
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
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
The irace package: Iterated racing for automatic algorithm configuration
López-Ibáñez, M., Dubois-Lacoste, J., Pérez Cáceres, L., Birattari, M., & Stützle, T. (2016) · Operations Research Perspectives · doi:10.1016/j.orp.2016.09.002 · Cited by 1,761
Knowles, J. (2006) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2005.851274 · Cited by 1,254
Evolutionary many-objective optimization: A short review
Ishibuchi, H., Tsukamoto, N., & Nojima, Y. (2008) · 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) · doi:10.1109/cec.2008.4631121 · Cited by 914
Covariance Matrix Adaptation for Multi-objective Optimization
Igel, C., Hansen, N., & Roth, S. (2007) · Evolutionary Computation · doi:10.1162/evco.2007.15.1.1 · Cited by 788
Metadata
- Authors
- Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., & da Fonseca, V. G.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2003-04
- Volume
- 7
- Issue
- 2
- Pages
- 117-132
- ISSN
- 1089-778X (ISSN-L)
- OpenAlex
- W2098907614
- Cited by
- 4,330
- References
- 1
- FWCI
- 63.37
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., & da Fonseca, V. G. (2003). Performance assessment of multiobjective optimizers: an analysis and review. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/tevc.2003.810758
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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.
