A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization
Chugh T, Jin Y, Miettinen K, Hakanen J, Sindhya K (2018)
IEEE Transactions on Evolutionary Computation 22(1): 129-142.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
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Autor*in
Chugh, Tinkle;
Jin, YaochuUniBi
;
Miettinen, Kaisa;
Hakanen, Jussi;
Sindhya, Karthik
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Abstract / Bemerkung
Surrogate-assisted evolutionary algorithms (SAEAs) have been developed mainly for solving expensive optimization problems where only a small number of real fitness evaluations are allowed. Most existing SAEAs are designed for solving low-dimensional single or multiobjective optimization problems, which are not well suited for many-objective optimization. This paper proposes a surrogate-assisted many-objective evolutionary algorithm that uses an artificial neural network to predict the dominance relationship between candidate solutions and reference solutions instead of approximating the objective values separately. The uncertainty information in prediction is taken into account together with the dominance relationship to select promising solutions to be evaluated using the real objective functions. Our simulation results demonstrate that the proposed algorithm outperforms the state-of-the-art evolutionary algorithms on a set of many-objective optimization test problems.
Erscheinungsjahr
2018
Zeitschriftentitel
IEEE Transactions on Evolutionary Computation
Band
22
Ausgabe
1
Seite(n)
129-142
ISSN
1089-778X
eISSN
1941-0026
Page URI
https://pub.uni-bielefeld.de/record/2978446
Zitieren
Chugh T, Jin Y, Miettinen K, Hakanen J, Sindhya K. A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization. IEEE Transactions on Evolutionary Computation. 2018;22(1):129-142.
Chugh, T., Jin, Y., Miettinen, K., Hakanen, J., & Sindhya, K. (2018). A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization. IEEE Transactions on Evolutionary Computation, 22(1), 129-142. https://doi.org/10.1109/TEVC.2016.2622301
Chugh, Tinkle, Jin, Yaochu, Miettinen, Kaisa, Hakanen, Jussi, and Sindhya, Karthik. 2018. “A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization”. IEEE Transactions on Evolutionary Computation 22 (1): 129-142.
Chugh, T., Jin, Y., Miettinen, K., Hakanen, J., and Sindhya, K. (2018). A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization. IEEE Transactions on Evolutionary Computation 22, 129-142.
Chugh, T., et al., 2018. A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization. IEEE Transactions on Evolutionary Computation, 22(1), p 129-142.
T. Chugh, et al., “A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization”, IEEE Transactions on Evolutionary Computation, vol. 22, 2018, pp. 129-142.
Chugh, T., Jin, Y., Miettinen, K., Hakanen, J., Sindhya, K.: A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization. IEEE Transactions on Evolutionary Computation. 22, 129-142 (2018).
Chugh, Tinkle, Jin, Yaochu, Miettinen, Kaisa, Hakanen, Jussi, and Sindhya, Karthik. “A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization”. IEEE Transactions on Evolutionary Computation 22.1 (2018): 129-142.