Abstract
To handle the constrained multi-objective evolutionary optimization problems, the authors firstly analyze Deb’s constrained-domination principle (DCDP) and point out that it more likely stick into local optimum on these problems with two or more disconnected feasible regions. Secondly, to handle constraints in multi-objective optimization problems (MOPs), a new constraint handling strategy is proposed, which keeps infeasible elitists to act as bridges connecting disconnected feasible regions besides feasible ones during optimization and adopts stochastic ranking to balance objectives and constraints in each generation. Finally, this strategy is applied to NSGA-II, and then is compared with DCDP on six benchmark constrained MOPs. Our results demonstrate that distribution and stability of the solutions are distinctly improved on the problems with two or more disconnected feasible regions, such as CTP6.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Schaffer, J.D.: Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. In: Proc. 1st International Conference on Genetic Algorithms, Hillsdale, pp. 93–100 (1985)
Zitzler, E., Thiele, L.: Multi-Objective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE. Trans. Evol. Comput. 3(4), 257–271 (1999)
Corne, D.W., Knowles, J.D.: The Pareto-envelope based selection algorithm for multiobjective optimization. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 839–848. Springer, Heidelberg (2000)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Tech. Rep. 103, Gloriastrasse 35, CH-8092 Zurich, Switzerland (2001)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Lu, H., Yen, G.G.: Rank-density-based multiobjective genetic algorithm and benchmark test function study. IEEE Trans. Evol. Comput. 7(4), 325–343 (2003)
Ho, S.-Y., Shu, L.-S., Chen, J.-H.: Intelligent evolutionary algorithms for large parameter optimization problems. IEEE Trans. Evol. Comput. 8(6), 522–541 (2004)
Bui, L.T., Branke, J., Abbass, H.A.: Multiobjective optimization for dynamic environments. In: CEC 2005, vol. 3, pp. 2349–2356 (2005)
Runarsson, T.P., Yao, X.: Stochastic Ranking for Constrained Evolutionary Optimization. IEEE Trans. Evol. Comput. 4(3), 284–294 (2000)
Deb, K., Pratap, A., Meyarivan, T.: Constrained Test Problems for Multi-objective Evolutionary Optimization. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 284–298. Springer, Heidelberg (2001)
Coello Coello, C.A.: Evolutionary multi-objective optimization: a historical view of the field. Computational Intelligence Magazine, IEEE 1(1), 28–36 (2006)
Fieldsend, J., Everson, R.M., Singh, S.: Using unconstrained elite archives for multiobjective optimization. IEEE Trans. Evol. Comput. 7(3), 305–323 (2003)
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., et al.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Evol. Comput. 7(2), 117–132 (2003)
Zhang, M., Geng, H., Luo, W., et al.: A Novel Search Biases Selection Strategy for Constrained Evolutionary Optimization. In: CEC 2006 (to appear)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Geng, H., Zhang, M., Huang, L., Wang, X. (2006). Infeasible Elitists and Stochastic Ranking Selection in Constrained Evolutionary Multi-objective Optimization. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_43
Download citation
DOI: https://doi.org/10.1007/11903697_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47331-2
Online ISBN: 978-3-540-47332-9
eBook Packages: Computer ScienceComputer Science (R0)