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Cited by 462

The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.

An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems

Deng, Wu, Zhang, Xiaoxiao, Zhou, Yongquan, Liu, Yi, Zhou, Xiangbing, Chen, Huiling, Zhao, Huimin·2022·Information Sciences
DOI 10.1016/j.ins.2021.11.052
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Research works printed but not linked: 16

  1. Peng, 2018, New automatic fuzzy relational clustering algorithms using multi-objective NSGA-II, Inf. Sci., 448–449, 112
  2. Li, 2021, Forecasting crude oil prices based on variational mode decomposition and random sparse Bayesian learning, Appl. Soft Comput., 113, 10.1016/j.asoc.2021.108032 · doi:10.1016/j.asoc.2021.108032
  3. Kumar, 2017, The elitist non-dominated sorting genetic algorithm with inheritance (i-NSGA-II) and its jumping gene adaptations for multi-objective optimization, Inf. Sci., 382–383, 15, 10.1016/j.ins.2016.12.003 · doi:10.1016/j.ins.2016.12.003
  4. Han, 2018, A multi-objective genetic algorithm based on fitting and interpolation, IEEE Access, 6, 22920, 10.1109/ACCESS.2018.2829262 · doi:10.1109/access.2018.2829262
  5. Metiaf, 2019, Searching with direction awareness: multi-objective genetic algorithm based on angle quantization and crowding distance MOGA-AQCD, IEEE Access, 7, 10196, 10.1109/ACCESS.2018.2890461 · doi:10.1109/access.2018.2890461
  6. Zang, 2019, A novel double-strand DNA genetic algorithm for multi-objective optimization, IEEE Access, 7, 18821, 10.1109/ACCESS.2019.2894726 · doi:10.1109/access.2019.2894726
  7. Li, 2018, A local search strategy based on density for NSGA2 algorithm, Control and Decision, 33, 60
  8. Devaurs, 2016, Optimal path planning in complex cost spaces with sampling-based algorithms, IEEE Trans. Autom. Sci. Eng., 13, 415, 10.1109/TASE.2015.2487881 · DOI
  9. F. Kudo, T. Yoshikawa and T. Furuhashi, “A study on analysis of design variables in Pareto solutions for conceptual design optimization problem of hybrid rocket engine,” IEEE Congress of Evolutionary Computation (CEC). IEEE, pp. 2558-2562, Jul. 2011. · doi:10.1109/cec.2011.5949936
  10. G. Sharma, V. Sharma, K. R. Pardasani and M. Alshehri, “Soft set based intelligent assistive model for multiobjective and multimodal transportation problem,” IEEE Access, vol. 8, pp. 102646-102656, May. 2020. · doi:10.1109/access.2020.2997302
  11. T. Sonoda and M. Nakata, “MOEA/D-S3: MOEA/D using SVM-based Surrogates adjusted to subproblems for many objective optimization,” 2020 IEEE Congress on Evolutionary Computation (CEC),pp. 1-8, Jul. 2020. · doi:10.1109/cec48606.2020.9185549
  12. J. Liang Q.Q. Guo C.T. Yue B.Y. Qu A self-organizing multi-objective particle swarm optimization algorithm for multi-modal multi-objective problems vol. 10941 2018 Springer, Cham Jun. · doi:10.1007/978-3-319-93815-8_52
  13. Wang, 2020, Preference-inspired coevolutionary algorithm with active diversity strategy for multi-objective multi-modal optimization, Inf. Sci., 546, 1148, 10.1016/j.ins.2020.09.075 · doi:10.1016/j.ins.2020.09.075
  14. R.Z. Shi, W. Lin, Q. Z. lin, Z. X. Zhu and J. Y. Chen, “Multimodal multi-objective optimization using a density-based one-by-one update strategy,” 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp. 295-301, Aug. 2019. · doi:10.1109/cec.2019.8790338
  15. Zhao, 2019, An improved adaptive NSGA-II with multi-population algorithm, Applied Intelligence, 49, 569, 10.1007/s10489-018-1263-6 · doi:10.1007/s10489-018-1263-6
  16. Larraín, 2020, Multiobjective optimization of a continuous kraft pulp digester using SPEA2, Comput. Chem. Eng., 143, 10.1016/j.compchemeng.2020.107086 · doi:10.1016/j.compchemeng.2020.107086

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Metadata

Authors
Deng, W., Zhang, X., Zhou, Y., Liu, Y., Zhou, X., Chen, H., & Zhao, H.
Venue
Information Sciences
Publisher
Elsevier BV
Published
2022-03
Volume
585
Pages
441-453
ISSN
0020-0255 (ISSN-L)
DOI
10.1016/j.ins.2021.11.052
OpenAlex
W3217374538
Cited by
462
References
26
FWCI
44.24
Type
journal article
Language
en
Open access
closed
Licence
Funders
NSFC

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CoChat. (2026). Citation check: An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems. https://app.cochat.ai/papers/10.1016/j.ins.2021.11.052
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Deng, W., Zhang, X., Zhou, Y., Liu, Y., Zhou, X., Chen, H., & Zhao, H. (2022). An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems. Information Sciences. https://doi.org/10.1016/j.ins.2021.11.052

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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.