Abstract
Over the past few years, researchers have developed a number of multi-objective evolutionary algorithms (MOEAs). Although most studies concentrated on solving unconstrained optimization problems, there exists a few studies where MOEAs have been extended to solve constrained optimization problems. As the constraint handling MOEAs gets popular, there is a need for developing test problems which can evaluate the algorithms well. In this paper, we review a number of test problems used in the literature and then suggest a set of tunable test problems for constraint handling. Finally, NSGA-II with an innovative constraint handling strategy is compared with a couple of existing algorithms in solving some of the test problems.
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Deb, K., Agrawal, S., Pratap, A., Meyarivan, T. (2000). A Fast Elitist Non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II.Proceedings of the Parallel Problem Solving from Nature VI Conference, pp. 849-858.
Deb, K., Pratap, A., Moitra, S. (2000). Mechanical component design for multi-ple objectives using elitist non-dominated sorting GA. Proceedings of the Parallel Problem Solving from Nature VI Conference, pp. 859–868.
Deb, K. (1999) Multi-objective genetic algorithms: Problem dificulties and con-struction of test Functions. Evolutionary Computation, 7(3), 205–230.
Deb, K. and Agrawal, R. B. (1995) Simulated binary crossover for continuous search space. Complex Systems, 9115–148.
Fonseca, C. M. and Fleming, P. J. (1993) Genetic algorithms for multi-objective optimization: Formulation, discussion and generalization. In Forrest, S., editor, Proceedings of the Fifth International Conference on Genetic Algorithms, pages 416–423, Morgan Kauffman, San Mateo, California.
Horn, J. and Nafploitis, N., and Goldberg, D. E. (1994) A niched Pareto genetic algorithm for multi-objective optimization. In Michalewicz, Z., editor, Proceedings of the First IEEE Conference on Evolutionary Computation, pages 82–87, IEEE Service Center, Piscataway, New Jersey.
Jiménez, F. and Verdegay, J. L. (1998). Constrained multiobejctive optimization by evolutionary algorithms. Proceedings of the International ICSC Symposium on Engineering of Intelligent Systems (EIS’98), pp. 266–271.
Knowles, J. and Corne, D. (1999) The Pareto archived evolution strategy: A new baseline algorithm for multiobjective optimisation. Proceedings of the 1999 Congress on Evolutionary Computation, Piscataway: New Jersey: IEEE Service Center, 98–105.
Osyczka, A. and Kundu, S. (1995). A new method to solve generalized multicri-teria optimization problems using the simple genetic algorithm. Structural Opti-mization(10). 94–99.
Ray, T., Kang, T., and Chye, S. (in press). Multiobjective design optimization by an evolutionary algorithm, Engineering Optimization.
Srinivas, N. and Deb, K. (1995). Multi-Objective function optimization using non-dominated sorting genetic algorithms. Evolutionary Computation(2), 221–248.
Tanaka, M. (1995). GA-based decision support system for multi-criteria op-timization. Proceedings of the International Conference on Systems, Man and Cybernetics-2, pp. 1556–1561.
Van Veldhuizen, D. (1999). Multiobjective evolutionary algorithms: Classifica-tions, analyses, and new innovations. PhD Dissertation and Technical Report No.AFIT/DS/ENG/99-01, Dayton, Ohio: Air Force Institute of Technology.
Zitzler, E. (1999). Evolutionary algorithms for multiobjective optimization: Meth-ods and applications. Doctoral thesis ETH NO. 13398, Zurich: Swiss Federal In-stitute of Technology (ETH), Aachen, Germany: Shaker Verlag.
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Deb, K., Pratap, A., Meyarivan, T. (2001). Constrained Test Problems for Multi-objective Evolutionary Optimization. In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds) Evolutionary Multi-Criterion Optimization. EMO 2001. Lecture Notes in Computer Science, vol 1993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44719-9_20
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DOI: https://doi.org/10.1007/3-540-44719-9_20
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