A new performance metric for user-preference based multi-objective evolutionary algorithms

Mohammadi, A, Omidvar, M and Li, X 2013, 'A new performance metric for user-preference based multi-objective evolutionary algorithms', in Carlos A. Coello Coello (ed.) Proceedings of 2013 IEEE Congress on Evolutionary Computation, Cancún, México, 20-23 June 2013, pp. 2825-2832.


Document type: Conference Paper
Collection: Conference Papers

Title A new performance metric for user-preference based multi-objective evolutionary algorithms
Author(s) Mohammadi, A
Omidvar, M
Li, X
Year 2013
Conference name 2013 IEEE Congress on Evolutionary Computation
Conference location Cancún, México
Conference dates 20-23 June 2013
Proceedings title Proceedings of 2013 IEEE Congress on Evolutionary Computation
Editor(s) Carlos A. Coello Coello
Publisher IEEE
Place of publication Piscataway, USA
Start page 2825
End page 2832
Total pages 8
Abstract In this paper, we propose a metric for evaluating the performance of user-preference based evolutionary multiobjective algorithms by defining a preferred region based on the location of a user-supplied reference point. This metric uses a composite front which is a type of reference set and is used as a replacement for the Pareto-optimal front. This composite front is constructed by extracting the non-dominated solutions from the merged solution sets of all algorithms that are to be compared. A preferred region is then defined on the composite front based on the location of a reference point. Once the preferred region is defined, existing evolutionary multi-objective performance metrics can be applied with respect to the preferred region. In this paper the performance of a cardinality-based metric, a distance-based metric, and a volume-based metric are compared against a baseline which relies on knowledge of the Pareto-optimal front. The experimental results show that the distance-based and the volume-based metrics are consistent with the baseline, showing meaningful comparisons. However, the cardinality-based approach shows some inconsistencies and is not suitable for comparing the algorithms.
Subjects Neural, Evolutionary and Fuzzy Computation
Keyword(s) Evolutionary multi-objective algorithms
Multi objective
Multi objective evolutionary algorithms
Nondominated solutions
Pareto-optimal front
Performance metrices
Performance metrics
Reference points
DOI - identifier 10.1109/CEC.2013.6557912
Copyright notice © 2013 IEEE
ISBN 9781479904532
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Citation counts: TR Web of Science Citation Count  Cited 21 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 11 times in Scopus Article | Citations
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