Abstract
The inverted generational distance (IGD) is a metric for assessing the quality of approximations to the Pareto front obtained by multi-objective optimization algorithms. The IGD has become the most commonly used metric in the context of many-objective problems, i.e., those with more than three objectives. The averaged Hausdorff distance and \(\textit{IGD}^+\) are variants of the IGD proposed in order to overcome its major drawbacks. In particular, the IGD is not Pareto compliant and its conclusions may strongly change depending on the size of the reference front. It is also well-known that different metrics assign more importance to various desired features of approximation fronts, and thus, they may disagree when ranking them. However, the precise behavior of the IGD variants is not well-understood yet. In particular, \(\textit{IGD}^+\), the only IGD variant that is weakly Pareto-compliant, has received significantly less attention. This paper presents an empirical analysis of the IGD variants. Our experiments evaluate how these metrics are affected by the most important factors that intuitively describe the quality of approximation fronts, namely, spread, distribution and convergence. The results presented here already reveal interesting insights. For example, we conclude that, in order to achieve small IGD or \(\textit{IGD}^+\) values, the approximation front size should match the reference front size.
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Notes
- 1.
To be more precise, the \(\epsilon \)-metric is only weakly Pareto-compliant, but we do not make a distinction between weakly and non-weakly Pareto-compliance in the remainder of this paper.
- 2.
In the following we assume maximization, without loss of generality.
References
Beume, N., Fonseca, C.M., López-Ibáñez, M., Paquete, L., Vahrenhold, J.: On the complexity of computing the hypervolume indicator. IEEE Trans. Evol. Comput. 13(5), 1075–1082 (2009)
Bezerra, L.C.T.: A component-wise approach to multi-objective evolutionary algorithms: from flexible frameworks to automatic design. Ph.D. thesis, IRIDIA, École polytechnique, Université Libre de Bruxelles, Belgium (2016)
Bezerra, L.C.T., López-Ibáñez, M., Stützle, T.: An empirical assessment of the properties of inverted generational distance indicators on multi- and many-objective optimization: supplementary material (2016). http://iridia.ulb.ac.be/supp/IridiaSupp. 2016-006/
Coello Coello, C.A., Reyes Sierra, M.: A study of the parallelization of a coevolutionary multi-objective evolutionary algorithm. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds.) MICAI 2004. LNCS (LNAI), vol. 2972, pp. 688–697. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24694-7_71
Deb, K., Jain, S.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577–601 (2014)
Dubois-Lacoste, J., López-Ibáñez, M., Stützle, T.: Improving the anytime behavior of two-phase local search. Ann. Math. Artif. Intell. 61(2), 125–154 (2011)
Ishibuchi, H., Akedo, N., Nojima, Y.: Behavior of multiobjective evolutionary algorithms on many-objective knapsack problems. IEEE Trans. Evol. Comput. 19(2), 264–283 (2015)
Ishibuchi, H., Masuda, H., Nojima, Y.: A study on performance evaluation ability of a modified inverted generational distance indicator. In: Silva, S. et al. (ed.) GECCO, pp. 695–702. ACM Press (2015)
Ishibuchi, H., Masuda, H., Tanigaki, Y., Nojima, Y.: Modified distance calculation in generational distance and inverted generational distance. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C.C. (eds.) EMO 2015. LNCS, vol. 9019, pp. 110–125. Springer, Heidelberg (2015). doi:10.1007/978-3-319-15892-1_8
Jiang, S., Ong, Y.S., Zhang, J., Feng, L.: Consistencies and contradictions of performance metrics in multiobjective optimization. IEEE Trans. Cybern. 44(12), 2391–2404 (2014)
Schütze, O., Esquivel, X., Lara, A., Coello, C.A.C.: Using the averaged Hausdorff distance as a performance measure in evolutionary multiobjective optimization. IEEE Trans. Evol. Comput. 16(4), 504–522 (2012)
Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective evolutionary algorithms: analyzing the state-of-the-art. Evol. Comput. 8(2), 125–147 (2000)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto evolutionary algorithm. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., da Fonseca, V.G.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7(2), 117–132 (2003)
Acknowledgments
The research presented in this paper has received funding from the COMEX project (P7/36) within the IAP Programme of BelSPO. T. Stützle acknowledges support from the Belgian F.R.S.-FNRS, of which he is a senior research associate.
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Bezerra, L.C.T., López-Ibáñez, M., Stützle, T. (2017). An Empirical Assessment of the Properties of Inverted Generational Distance on Multi- and Many-Objective Optimization. In: Trautmann, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2017. Lecture Notes in Computer Science(), vol 10173. Springer, Cham. https://doi.org/10.1007/978-3-319-54157-0_3
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