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. 2018 Sep 10;8:13513. doi: 10.1038/s41598-018-31902-8

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Visualization of Pareto front (center) and normalized Pareto front distance (right). Only few competitors, in this case five, contribute to the set of interesting solutions, which are identified by the Pareto front spanned up by two dimensions (R value and running time). BI is the most accurate competitor and also the slowest competitor. DEG, on the other hand, estimates the robustness very quick, but less accurate. ABI, DEGI, and ND provide a trade-off between fast execution and accurate R values. The y-axis (running time) is log-scaled. The normalized Pareto front makes the results comparable over different networks.