A Many-Objective Evolutionary Algorithm Using A One-by-One Selection Strategy
Liu Y, Gong D, Sun J, Jin Y (2017)
IEEE Transactions on Cybernetics 47(9): 2689-2702.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
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Autor*in
Liu, Yiping;
Gong, Dunwei;
Sun, Jing;
Jin, YaochuUniBi ![](https://pub.uni-bielefeld.de/images/icon_orcid.png)
![](https://pub.uni-bielefeld.de/images/icon_orcid.png)
Abstract / Bemerkung
Most existing multiobjective evolutionary algorithms experience difficulties in solving many-objective optimization problems due to their incapability to balance convergence and diversity in the high-dimensional objective space. In this paper, we propose a novel many-objective evolutionary algorithm using a one-by-one selection strategy. The main idea is that in the environmental selection, offspring individuals are selected one by one based on a computationally efficient convergence indicator to increase the selection pressure toward the Pareto optimal front. In the one-by-one selection, once an individual is selected, its neighbors are de-emphasized using a niche technique to guarantee the diversity of the population, in which the similarity between individuals is evaluated by means of a distribution indicator. In addition, different methods for calculating the convergence indicator are examined and an angle-based similarity measure is adopted for effective evaluations of the distribution of solutions in the high-dimensional objective space. Moreover, corner solutions are utilized to enhance the spread of the solutions and to deal with scaled optimization problems. The proposed algorithm is empirically compared with eight state-of-the-art many-objective evolutionary algorithms on 80 instances of 16 benchmark problems. The comparative results demonstrate that the overall performance of the proposed algorithm is superior to the compared algorithms on the optimization problems studied in this paper.
Erscheinungsjahr
2017
Zeitschriftentitel
IEEE Transactions on Cybernetics
Band
47
Ausgabe
9
Seite(n)
2689-2702
ISSN
2168-2267
eISSN
2168-2275
Page URI
https://pub.uni-bielefeld.de/record/2978472
Zitieren
Liu Y, Gong D, Sun J, Jin Y. A Many-Objective Evolutionary Algorithm Using A One-by-One Selection Strategy. IEEE Transactions on Cybernetics. 2017;47(9):2689-2702.
Liu, Y., Gong, D., Sun, J., & Jin, Y. (2017). A Many-Objective Evolutionary Algorithm Using A One-by-One Selection Strategy. IEEE Transactions on Cybernetics, 47(9), 2689-2702. https://doi.org/10.1109/TCYB.2016.2638902
Liu, Yiping, Gong, Dunwei, Sun, Jing, and Jin, Yaochu. 2017. “A Many-Objective Evolutionary Algorithm Using A One-by-One Selection Strategy”. IEEE Transactions on Cybernetics 47 (9): 2689-2702.
Liu, Y., Gong, D., Sun, J., and Jin, Y. (2017). A Many-Objective Evolutionary Algorithm Using A One-by-One Selection Strategy. IEEE Transactions on Cybernetics 47, 2689-2702.
Liu, Y., et al., 2017. A Many-Objective Evolutionary Algorithm Using A One-by-One Selection Strategy. IEEE Transactions on Cybernetics, 47(9), p 2689-2702.
Y. Liu, et al., “A Many-Objective Evolutionary Algorithm Using A One-by-One Selection Strategy”, IEEE Transactions on Cybernetics, vol. 47, 2017, pp. 2689-2702.
Liu, Y., Gong, D., Sun, J., Jin, Y.: A Many-Objective Evolutionary Algorithm Using A One-by-One Selection Strategy. IEEE Transactions on Cybernetics. 47, 2689-2702 (2017).
Liu, Yiping, Gong, Dunwei, Sun, Jing, and Jin, Yaochu. “A Many-Objective Evolutionary Algorithm Using A One-by-One Selection Strategy”. IEEE Transactions on Cybernetics 47.9 (2017): 2689-2702.