Combination of EDA and DE for continuous biobjective optimization
Zhou A, Zhang Q, Jin Y, Sendhoff B (2008)
In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence). IEEE: 1447-1454.
Konferenzbeitrag
| Veröffentlicht | Englisch
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Autor*in
Zhou, Aimin;
Zhang, Qingfu;
Jin, YaochuUniBi 
;
Sendhoff, Bernhard
;
Sendhoff, BernhardAbstract / Bemerkung
The Pareto front (Pareto set) of a continuous optimization problem with m objectives is a (m-1) dimensional piecewise continuous manifold in the objective space (the decision space) under some mild conditions. Based on this regularity property in the objective space, we have recently developed several multiobjective estimation of distribution algorithms (EDAs). However, this property has not been utilized in the decision space. Using the regularity property in both the objective and decision space, this paper proposes a simple EDA for multiobjective optimization. Since the location information has not efficiently used in EDAs, a combination of EDA and differential evolution (DE) is suggested for improving the algorithmic performance. The hybrid method and the pure EDA method proposed in this paper, and a DE based method are compared on several test instances. Experimental results have shown that the algorithm with the proposed strategy is very promising.
Erscheinungsjahr
2008
Titel des Konferenzbandes
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)
Seite(n)
1447-1454
Konferenz
2008 IEEE Congress on Evolutionary Computation (CEC)
Konferenzort
Hong Kong, China
ISBN
978-1-4244-1822-0
Page URI
https://pub.uni-bielefeld.de/record/2978636
Zitieren
Zhou A, Zhang Q, Jin Y, Sendhoff B. Combination of EDA and DE for continuous biobjective optimization. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence). IEEE; 2008: 1447-1454.
Zhou, A., Zhang, Q., Jin, Y., & Sendhoff, B. (2008). Combination of EDA and DE for continuous biobjective optimization. 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 1447-1454. IEEE. https://doi.org/10.1109/CEC.2008.4630984
Zhou, Aimin, Zhang, Qingfu, Jin, Yaochu, and Sendhoff, Bernhard. 2008. “Combination of EDA and DE for continuous biobjective optimization”. In 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 1447-1454. IEEE.
Zhou, A., Zhang, Q., Jin, Y., and Sendhoff, B. (2008). “Combination of EDA and DE for continuous biobjective optimization” in 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) (IEEE), 1447-1454.
Zhou, A., et al., 2008. Combination of EDA and DE for continuous biobjective optimization. In 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence). IEEE, pp. 1447-1454.
A. Zhou, et al., “Combination of EDA and DE for continuous biobjective optimization”, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), IEEE, 2008, pp.1447-1454.
Zhou, A., Zhang, Q., Jin, Y., Sendhoff, B.: Combination of EDA and DE for continuous biobjective optimization. 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence). p. 1447-1454. IEEE (2008).
Zhou, Aimin, Zhang, Qingfu, Jin, Yaochu, and Sendhoff, Bernhard. “Combination of EDA and DE for continuous biobjective optimization”. 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence). IEEE, 2008. 1447-1454.
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