Abstract
For evaluating performance of a multi-objective optimization for finding the entire efficient front, a number of metrics, such as hyper-volume, inverse generational distance, etc. exists. However, for evaluating an EMO algorithm for finding a subset of the efficient frontier, the existing metrics are inadequate. There does not exist many performance metrics for evaluating a partial preferred efficient set. In this paper, we suggest a metric which can be used for such purposes for both attainable and unattainable reference points. Results on a number of two-objective problems reveal its working principle and its importance in assessing different algorithms. The results are promising and encouraging for its further use.
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Acknowledgment
Authors thank Debayan Deb, an undergraduate student of Michigan State of University, USA, for assisting in computer programming of the R-HV metric concept.
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Deb, K., Siegmund, F., Ng, A.H.C. (2015). R-HV: A Metric for Computing Hyper-volume for Reference Point Based EMOs. In: Panigrahi, B., Suganthan, P., Das, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science(), vol 8947. Springer, Cham. https://doi.org/10.1007/978-3-319-20294-5_9
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DOI: https://doi.org/10.1007/978-3-319-20294-5_9
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