The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.
A Survey of Evolutionary Algorithms for Multi-Objective Optimization Problems With Irregular Pareto Fronts
Abstract
Evolutionary algorithms have been shown to be very successful in solving multi-objective optimization problems (MOPs). However, their performance often deteriorates when solving MOPs with irregular Pareto fronts. To remedy this issue, a large body of research has been performed in recent years and many new algorithms have been proposed. This paper provides a comprehensive survey of the research on MOPs with irregular Pareto fronts. We start with a brief introduction to the basic concepts, followed by a summary of the benchmark test problems with irregular problems, an analysis of the causes of the irregularity, and real-world optimization problems with irregular Pareto fronts. Then, a taxonomy of the existing methodologies for handling irregular problems is given and representative algorithms are reviewed with a discussion of their strengths and weaknesses. Finally, open challenges are pointed out and a few promising future directions are suggested.
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Cites
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Cited by
Hybrid approaches to optimization and machine learning methods: a systematic literature review
Azevedo, B. F., Rocha, A. M. A. C., & Pereira, A. I. (2024) · Machine Learning · doi:10.1007/s10994-023-06467-x · Cited by 291
Metadata
- Authors
- Hua, Y., Liu, Q., Hao, K., & Jin, Y.
- Venue
- IEEE/CAA Journal of Automatica Sinica
- Published
- 2021-02
- Volume
- 8
- Issue
- 2
- Pages
- 303-318
- ISSN
- 2329-9266 (ISSN-L)
- 2329-9274
- OpenAlex
- W3120225493
- Cited by
- 346
- References
- 77
- FWCI
- 28.37
- Type
- journal article
- Language
- en
- Open access
- closed
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- Hua, Y., Liu, Q., Hao, K., & Jin, Y. (2021). A Survey of Evolutionary Algorithms for Multi-Objective Optimization Problems With Irregular Pareto Fronts. IEEE/CAA Journal of Automatica Sinica. https://doi.org/10.1109/jas.2021.1003817
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