The DOI matches its Crossref record; 156 of the 156 references it cites were found in a registry or an index.
Evolutionary Large-Scale Multi-Objective Optimization: A Survey
Abstract
Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in solving various optimization problems, but their performance may deteriorate drastically when tackling problems containing a large number of decision variables. In recent years, much effort been devoted to addressing the challenges brought by large-scale multi-objective optimization problems. This article presents a comprehensive survey of stat-of-the-art MOEAs for solving large-scale multi-objective optimization problems. We start with a categorization of these MOEAs into decision variable grouping based, decision space reduction based, and novel search strategy based MOEAs, discussing their strengths and weaknesses. Then, we review the benchmark problems for performance assessment and a few important and emerging applications of MOEAs for large-scale multi-objective optimization. Last, we discuss some remaining challenges and future research directions of evolutionary large-scale multi-objective optimization.
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Cites
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Cited by
A Survey on Evolutionary Constrained Multiobjective Optimization
Liang, J., Ban, X., Yu, K., Qu, B., Qiao, K., Yue, C., Chen, K., & Tan, K. C. (2023) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2022.3155533 · Cited by 438
Self-citationZhao, R., Tao, S., & Li, P. (2025) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2024.102942 · Cited by 22
Metadata
- Authors
- Tian, Y., Si, L., Zhang, X., Cheng, R., He, C., Tan, K. C., & Jin, Y.
- Venue
- ACM Computing Surveys
- Published
- 2021-10-04
- Volume
- 54
- Issue
- 8
- Pages
- 1-34
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W3201929238
- Cited by
- 401
- References
- 156
- FWCI
- 30.87
- Type
- journal article
- Language
- en
- Open access
- closed
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