The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Scalable multi-objective optimization test problems
Abstract
After adequately demonstrating the ability to solve different two-objective optimization problems, multi-objective evolutionary algorithms (MOEAs) must show their efficacy in handling problems having more than two objectives. In this paper, we suggest three different approaches for systematically designing test problems for this purpose. The simplicity of construction, scalability to any number of decision variables and objectives, knowledge of exact shape and location of the resulting Pareto-optimal front, and ability to control difficulties in both converging to the true Pareto-optimal front and maintaining a widely distributed set of solutions are the main features of the suggested test problems. Because of these features, they should be useful in various research activities on MOEAs, such as testing the performance of a new MOEA, comparing different MOEAs, and having a better understanding of the working principles of MOEAs.
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Citations
Cites
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
Self-citationMulti-Objective Optimization Using Evolutionary Algorithms
Deb, K., & Kalyanmoy, D. (2001) · Andalas University Repository (Andalas University) · Cited by 14,918
Self-citationA Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II
Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45356-3_83 · Cited by 5,289
Self-citationSPEA2: Improving the strength pareto evolutionary algorithm
Zitzler, E., Laumanns, M., & Thiele, L. (2001) · doi:10.3929/ethz-a-004284029 · Cited by 5,008
Self-citationAn Overview of Evolutionary Algorithms in Multiobjective Optimization
Fonseca, C. M., & Fleming, P. J. (1995) · Evolutionary Computation · doi:10.1162/evco.1995.3.1.1 · Cited by 2,266
Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems
Deb, K. (1999) · Evolutionary Computation · doi:10.1162/evco.1999.7.3.205 · Cited by 1,451
Self-citationMultiobjective evolutionary algorithms: classifications, analyses, and new innovations
Lamont, G. B., & Veldhuizen, D. A. V. (1999) · Cited by 1,212
A variant of evolution strategies for vector optimization
Kursawe, F. (2006) · Lecture Notes in Computer Science · doi:10.1007/bfb0029752 · Cited by 597
Multi-objective optimization by genetic algorithms: a review
Tamaki, H., Kita, H., & Kobayashi, S. (2002) · Proceedings of IEEE International Conference on Evolutionary Computation · doi:10.1109/icec.1996.542653 · Cited by 292
GA-based decision support system for multicriteria optimization
Tanaka, M., Watanabe, H., Furukawa, Y., & Tanino, T. (2002) · 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century · doi:10.1109/icsmc.1995.537993 · Cited by 248
Cited by
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Zhang, Q., & Li, H. (2007) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2007.892759 · Cited by 10,069
A review of multiobjective test problems and a scalable test problem toolkit
Huband, S., Hingston, P., Barone, L., & While, L. (2006) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2005.861417 · Cited by 2,090
Evolutionary multi-objective optimization: a historical view of the field
Coello Coello, C. A. (2006) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2006.1597059 · Cited by 1,446
Evolutionary many-objective optimization: A short review
Ishibuchi, H., Tsukamoto, N., & Nojima, Y. (2008) · 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) · doi:10.1109/cec.2008.4631121 · Cited by 914
Expensive Multiobjective Optimization by MOEA/D With Gaussian Process Model
Zhang, Q., Liu, W., Tsang, E., & Virginas, B. (2010) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2009.2033671 · Cited by 746
Metadata
- Authors
- Deb, K., Thiele, L., Laumanns, M., & Zitzler, E.
- Venue
- Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)
- Publisher
- IEEE
- Published
- 2003-06-25
- Volume
- 1
- Pages
- 825-830
- OpenAlex
- W1846834309
- Cited by
- 1,627
- References
- 11
- FWCI
- 18.61
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Deb, K., Thiele, L., Laumanns, M., & Zitzler, E. (2003). Scalable multi-objective optimization test problems. Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600). https://doi.org/10.1109/cec.2002.1007032
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