The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Many-Objective Evolutionary Algorithms: A Survey
Open accessAbstract
Multiobjective evolutionary algorithms (MOEAs) have been widely used in real-world applications. However, most MOEAs based on Pareto-dominance handle many-objective problems (MaOPs) poorly due to a high proportion of incomparable and thus mutually nondominated solutions. Recently, a number of many-objective evolutionary algorithms (MaOEAs) have been proposed to deal with this scalability issue. In this article, a survey of MaOEAs is reported. According to the key ideas used, MaOEAs are categorized into seven classes: relaxed dominance based, diversity-based, aggregation-based, indicator-based, reference set based, preference-based, and dimensionality reduction approaches. Several future research directions in this field are also discussed.
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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
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
Techniques for highly multiobjective optimisation: some nondominated points are better than others
Corne, D. W., & Knowles, J. D. (2007) · Proceedings of the 9th annual conference on Genetic and evolutionary computation · doi:10.1145/1276958.1277115 · Cited by 273
Cited by
A review on genetic algorithm: past, present, and future
Katoch, S., Chauhan, S. S., & Kumar, V. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10139-6 · Cited by 4,655
Tian, Y., Cheng, R., Zhang, X., Cheng, F., & Jin, Y. (2018) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2017.2749619 · Cited by 699
Push and pull search for solving constrained multi-objective optimization problems
Fan, Z., Li, W., Cai, X., Li, H., Wei, C., Zhang, Q., Deb, K., & Goodman, E. (2019) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2018.08.017 · Cited by 594
A practical guide to multi-objective reinforcement learning and planning
Hayes, C. F., Rădulescu, R., Bargiacchi, E., Källström, J., Macfarlane, M., Reymond, M., Verstraeten, T., Zintgraf, L. M., Dazeley, R., Heintz, F., et al. (2022) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-022-09552-y · Cited by 400
“…the task of handling problems with many objectives (usually defined as four or more objectives) has emerged as a distinct sub-field, in recognition that algorithms which work well for a small number of objectives may scale poorly to many objectives [Von Lücken et al., 2014, Li et al., 2015].”
BackgroundBenchmarking in Optimization: Best Practice and Open Issues
Bartz-Beielstein, T., Doerr, C., Berg, D. V. D., Bossek, J., Chandrasekaran, S., Eftimov, T., Fischbach, A., Kerschke, P., La Cava, W., Lopez-Ibanez, M., et al. (2020) · arXiv · doi:10.48550/arxiv.2007.03488 · Cited by 214
Metadata
- Authors
- Li, B., Li, J., Tang, K., & Yao, X.
- Venue
- ACM Computing Surveys
- Published
- 2015-09-29
- Volume
- 48
- Issue
- 1
- Pages
- 1-35
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2040622444
- Cited by
- 815
- References
- 3
- FWCI
- 63.55
- Type
- journal article
- Language
- en
- Open access
- bronze
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