The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Performance metrics in multi-objective optimization
Abstract
In the last decades, a large number of metrics has been proposed to compare the performance of different evolutionary approaches in multi-objective optimization. This situation leads to difficulties when comparisons among the output of different algorithms are needed and appropriate metrics must be selected to perform those comparisons. Hence, no complete agreement on what metrics should be used exists. This paper presents a review and analysis of 54 multi-objective-optimization metrics in the specialized literature, discussing the usage, tendency and advantages/disadvantages of the most cited ones in order to give researchers enough information when choosing metrics is necessary. The review process performed in this work indicates that the hypervolume is the most used metric, followed by the generational distance, the epsilon indicator and the inverted generational distance.
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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
Cited by
Laszczyk, M., & Myszkowski, P. B. (2019) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2019.04.001 · Cited by 79
Metadata
- Authors
- Riquelme, N., Von Lucken, C., & Baran, B.
- Venue
- 2015 Latin American Computing Conference (CLEI)
- Publisher
- IEEE
- Published
- 2015-10
- Pages
- 1-11
- OpenAlex
- W2207649084
- Cited by
- 444
- References
- 1
- FWCI
- 17.77
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Riquelme, N., Von Lucken, C., & Baran, B. (2015). Performance metrics in multi-objective optimization. 2015 Latin American Computing Conference (CLEI). https://doi.org/10.1109/clei.2015.7360024
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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