Abstract
In this paper, we propose a hybrid reference-point based evolutionary multi-objective optimization (EMO) algorithm coupled with the classical SQP procedure for solving constrained single-objective optimization problems. The reference point based EMO procedure allows the procedure to focus its search near the constraint boundaries, while the SQP methodology acts as a local search to improve the solutions. The hybrid procedure is shown to solve a number of state-of-the-art constrained test problems with success. In some of the difficult problems, the SQP procedure alone is unable to find the true optimum, while the combined procedure solves them repeatedly. The proposed procedure is now ready to be tested on real-world optimization problems.
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References
Branke, J., Kauβler, T., Schmeck, H.: Guidance in evolutionary multi-objective optimization. Advances in Engineering Software 32, 499–507 (2001)
Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiley, Chichester, UK (2001)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Deb, K., Karthik, S., Okabe, T.: Self-adaptive simulated binary crossover for real-parameter optimization. In: GECCO-2007. Proceedings of the Genetic and Evolutionary Computation Conference, July 7-11, The Association of Computing Machinery (ACM), New York (in press, 2007)
Deb, K., Sundar, J., Uday, N., Chaudhuri, S.: Reference point based multi-objective optimization using evolutionary algorithms. International Journal of Computational Intelligence Research (IJCIR) 2(6), 273–286 (2006)
Liang, J.J., Runarsson, T.P., Mezura-Montes, E., Clerc, M., Suganthan, P.N., Coello Coello, C.A., Deb, K.: Special session on constrained real-parameter optimization (2006), http://www.ntu.edu.sg/home/epnsugan/
Wierzbicki, A.P.: The use of reference objectives in multiobjective optimization. In: Fandel, G., Gal, T. (eds.) Multiple Criteria Decision Making Theory and Applications, pp. 468–486. Springer, Berlin (1980)
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Deb, K., Lele, S., Datta, R. (2007). A Hybrid Evolutionary Multi-objective and SQP Based Procedure for Constrained Optimization. In: Kang, L., Liu, Y., Zeng, S. (eds) Advances in Computation and Intelligence. ISICA 2007. Lecture Notes in Computer Science, vol 4683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74581-5_4
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DOI: https://doi.org/10.1007/978-3-540-74581-5_4
Publisher Name: Springer, Berlin, Heidelberg
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