A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization
Zhang X, Tian Y, Jin Y (2015)
IEEE Transactions on Evolutionary Computation 19(6): 761-776.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
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Autor*in
Zhang, Xingyi;
Tian, Ye;
Jin, YaochuUniBi ![](https://pub.uni-bielefeld.de/images/icon_orcid.png)
![](https://pub.uni-bielefeld.de/images/icon_orcid.png)
Abstract / Bemerkung
Evolutionary algorithms (EAs) have shown to be promising in solving many-objective optimization problems (MaOPs), where the performance of these algorithms heavily depends on whether solutions that can accelerate convergence toward the Pareto front and maintaining a high degree of diversity will be selected from a set of nondominated solutions. In this paper, we propose a knee point-driven EA to solve MaOPs. Our basic idea is that knee points are naturally most preferred among nondominated solutions if no explicit user preferences are given. A bias toward the knee points in the nondominated solutions in the current population is shown to be an approximation of a bias toward a large hypervolume, thereby enhancing the convergence performance in many-objective optimization. In addition, as at most one solution will be identified as a knee point inside the neighborhood of each solution in the nondominated front, no additional diversity maintenance mechanisms need to be introduced in the proposed algorithm, considerably reducing the computational complexity compared to many existing multiobjective EAs for many-objective optimization. Experimental results on 16 test problems demonstrate the competitiveness of the proposed algorithm in terms of both solution quality and computational efficiency.
Erscheinungsjahr
2015
Zeitschriftentitel
IEEE Transactions on Evolutionary Computation
Band
19
Ausgabe
6
Seite(n)
761-776
ISSN
1089-778X
eISSN
1941-0026
Page URI
https://pub.uni-bielefeld.de/record/2978524
Zitieren
Zhang X, Tian Y, Jin Y. A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization. IEEE Transactions on Evolutionary Computation. 2015;19(6):761-776.
Zhang, X., Tian, Y., & Jin, Y. (2015). A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization. IEEE Transactions on Evolutionary Computation, 19(6), 761-776. https://doi.org/10.1109/TEVC.2014.2378512
Zhang, Xingyi, Tian, Ye, and Jin, Yaochu. 2015. “A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization”. IEEE Transactions on Evolutionary Computation 19 (6): 761-776.
Zhang, X., Tian, Y., and Jin, Y. (2015). A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization. IEEE Transactions on Evolutionary Computation 19, 761-776.
Zhang, X., Tian, Y., & Jin, Y., 2015. A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization. IEEE Transactions on Evolutionary Computation, 19(6), p 761-776.
X. Zhang, Y. Tian, and Y. Jin, “A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization”, IEEE Transactions on Evolutionary Computation, vol. 19, 2015, pp. 761-776.
Zhang, X., Tian, Y., Jin, Y.: A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization. IEEE Transactions on Evolutionary Computation. 19, 761-776 (2015).
Zhang, Xingyi, Tian, Ye, and Jin, Yaochu. “A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization”. IEEE Transactions on Evolutionary Computation 19.6 (2015): 761-776.