The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization
Abstract
Many-objective optimization problems (ManyOPs) refer, usually, to those multiobjective problems (MOPs) with more than three objectives. Their large numbers of objectives pose challenges to multiobjective evolutionary algorithms (MOEAs) in terms of convergence, diversity, and complexity. Most existing MOEAs can only perform well in one of those three aspects. In view of this, we aim to design a more balanced MOEA on ManyOPs in all three aspects at the same time. Among the existing MOEAs, the two-archive algorithm (Two_Arch) is a low-complexity algorithm with two archives focusing on convergence and diversity separately. Inspired by the idea of Two_Arch, we propose a significantly improved two-archive algorithm (i.e., Two_Arch2) for ManyOPs in this paper. In our Two_Arch2, we assign different selection principles (indicator-based and Pareto-based) to the two archives. In addition, we design a new Lp-norm-based (p <; 1) diversity maintenance scheme for ManyOPs in Two_Arch2. In order to evaluate the performance of Two_Arch2 on ManyOPs, we have compared it with several MOEAs on a wide range of benchmark problems with different numbers of objectives. The experimental results show that Two_Arch2 can cope with ManyOPs (up to 20 objectives) with satisfactory convergence, diversity, and complexity.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Multi-Objective Optimization Using Evolutionary Algorithms
Deb, K., & Kalyanmoy, D. (2001) · Andalas University Repository (Andalas University) · Cited by 14,918
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
Deb, K., & Jain, H. (2014) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2013.2281535 · Cited by 7,133
Nonparametric Statistical Methods
(1999) · Technometrics · doi:10.1080/00401706.1999.10485954 · Cited by 6,090
Nonlinear Multiobjective Optimization
Miettinen, K. (1998) · International Series in Operations Research & Management Science · doi:10.1007/978-1-4615-5563-6 · Cited by 4,710
Nonlinear Multiobjective Optimization
Mardle, S., & Miettinen, K. M. (2000) · The Journal of the Operational Research Society · doi:10.2307/254267 · Cited by 3,049
Nonparametric Statistical Methods.
Knott, M., Hollander, M., & Wolfe, D. A. (1974) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2344557 · Cited by 2,662
Indicator-Based Selection in Multiobjective Search
Zitzler, E., & Künzli, S. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-30217-9_84 · Cited by 2,259
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
Cited by
Platemo: a matlab platform for evolutionary multi-objective optimization [educational forum]
Tian, Y., Cheng, R., Zhang, X., & Jin, Y. (2017) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2017.2742868 · Cited by 2,777
A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization
Cheng, R., Jin, Y., Olhofer, M., & Sendhoff, B. (2016) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2016.2519378 · Cited by 1,760
A Multimodal Multiobjective Evolutionary Algorithm Using Two-Archive and Recombination Strategies
Liu, Y., Yen, G. G., & Gong, D. (2019) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2018.2879406 · Cited by 325
Pareto or Non-Pareto: Bi-Criterion Evolution in Multiobjective Optimization
Li, M., Yang, S., & Liu, X. (2016) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2015.2504730 · Cited by 309
Lin, Q., Liu, S., Zhu, Q., Tang, C., Song, R., Chen, J., Coello, C. A. C., Wong, K.-C., & Zhang, J. (2018) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2016.2631279 · Cited by 309
Metadata
- Authors
- Wang, H., Jiao, L., & Yao, X.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2015-08
- Volume
- 19
- Issue
- 4
- Pages
- 524-541
- ISSN
- 1089-778X (ISSN-L)
- 1941-0026
- OpenAlex
- W1493761729
- Cited by
- 558
- References
- 45
- FWCI
- 25.79
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization. https://app.cochat.ai/papers/10.1109/tevc.2014.2350987
- The paper
- Wang, H., Jiao, L., & Yao, X. (2015). Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/tevc.2014.2350987
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
