The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Use of cooperative coevolution for solving large scale multiobjective optimization problems
Abstract
Many real-world multi-objective optimization problems have hundreds or even thousands of decision variables, which contrast with the current practice of multi-objective metaheuristics whose performance is typically assessed using benchmark problems with a relatively low number of decision variables (normally, no more than 30). In this paper, we propose a cooperative coevolution framework that is capable of optimizing large scale (in decision variable space) multi-objective optimization problems. We adopt a benchmark that is scalable in the number of decision variables (the ZDT test suite) and compare our proposed algorithm with respect to two state-of-the-art multi-objective evolutionary algorithms (GDE3 and NSGA-II) when using a large number of decision variables (from 200 up to 5000). The results clearly indicate that our proposed approach is effective as well as efficient for solving large scale multi-objective optimization problems.
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Cites
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
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
Zitzler, E., & Thiele, L. (1999) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.797969 · Cited by 8,798
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Zitzler, E., Deb, K., & Thiele, L. (2000) · Evolutionary Computation · doi:10.1162/106365600568202 · Cited by 5,697
A 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
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
HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization
Bader, J., & Zitzler, E. (2011) · Evolutionary Computation · doi:10.1162/evco_a_00009 · Cited by 2,081
GENETIC ALGORITHMS FOR MULTI-OBJECTIVE OPTIMIZATION: FORMULATION, DISCUSSION, AND GENERALIZATION
Fonseca, C. M., & Fleming, P. J. (1993) · Cited by 2,056
A cooperative coevolutionary approach to function optimization
Potter, M. A., & Jong, K. A. (1994) · Lecture Notes in Computer Science · doi:10.1007/3-540-58484-6_269 · Cited by 1,510
GDE3: The third Evolution Step of Generalized Differential Evolution
Kukkonen, S., & Lampinen, J. (2005) · 2005 IEEE Congress on Evolutionary Computation · doi:10.1109/cec.2005.1554717 · Cited by 646
Introduction to the Practice of Statistics
(2001) · Technometrics · doi:10.1198/tech.2001.s606 · Cited by 413
Cited by
A Survey on Cooperative Co-Evolutionary Algorithms
Ma, X., Li, X., Zhang, Q., Tang, K., Liang, Z., Xie, W., & Zhu, Z. (2019) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2018.2868770 · Cited by 336
Metadata
- Authors
- Antonio, L. M., & Coello, C. A. C.
- Venue
- 2013 IEEE Congress on Evolutionary Computation
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 2758-2765
- OpenAlex
- W1971537182
- Cited by
- 276
- References
- 15
- FWCI
- 6.29
- Type
- conference paper
- Language
- en
- Open access
- closed
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