The DOI matches its Crossref record; 106 of the 106 references it cites were found in a registry or an index.
A Survey on Evolutionary Constrained Multiobjective Optimization
Open accessAbstract
Handling constrained multiobjective optimization problems (CMOPs) is extremely challenging, since multiple conflicting objectives subject to various constraints require to be simultaneously optimized. To deal with CMOPs, numerous constrained multiobjective evolutionary algorithms (CMOEAs) have been proposed in recent years, and they have achieved promising performance. However, there has been few literature on the systematic review of the related studies currently. This article provides a comprehensive survey for evolutionary constrained multiobjective optimization. We first review a large number of CMOEAs through categorization and analyze their advantages and drawbacks in each category. Then, we summarize the benchmark test problems and investigate the performance of different constraint handling techniques (CHTs) and different algorithms, followed by some emerging and representative applications of CMOEAs. Finally, we discuss some new challenges and point out some directions of the future research in the field of evolutionary constrained multiobjective optimization.
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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
No free lunch theorems for optimization
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Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms
Srinivas, N., & Deb, K. (1994) · Evolutionary Computation · doi:10.1162/evco.1994.2.3.221 · Cited by 6,935
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Zitzler, E., Deb, K., & Thiele, L. (2000) · Evolutionary Computation · doi:10.1162/106365600568202 · Cited by 5,697
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Handling multiple objectives with particle swarm optimization
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Platemo: a matlab platform for evolutionary multi-objective optimization [educational forum]
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Machine Learning Algorithms - A Review
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Scalable Test Problems for Evolutionary Multiobjective Optimization
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Petrović, M., Jokić, A., Miljković, Z., & Kulesza, Z. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109784 · Cited by 50
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Metadata
- Authors
- Liang, J., Ban, X., Yu, K., Qu, B., Qiao, K., Yue, C., Chen, K., & Tan, K. C.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2023-04
- Volume
- 27
- Issue
- 2
- Pages
- 201-221
- ISSN
- 1089-778X (ISSN-L)
- 1941-0026
- OpenAlex
- W4214941022
- Cited by
- 438
- References
- 106
- FWCI
- 44.47
- Type
- journal article
- Language
- en
- Open access
- hybrid
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