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Cited by 48Advanced Multi-Objective Optimization AlgorithmsSatellite Communication SystemsScheduling and Optimization Algorithms

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

A large-scale multiobjective satellite data transmission scheduling algorithm based on SVM+NSGA-II

Zhang, Jiawei, Xing, Lining, Peng, Guansheng, Yao, Feng, Chen, Cheng·2019·Swarm and Evolutionary Computation
DOI 10.1016/j.swevo.2019.100560
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Crossref · 10.1016/j.swevo.2019.100560

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1 of 1 references verified0 citation errors · 0 not checkable

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  • The computational cost of MOEAs is much heavier than algorithms in the first two categories and cannot be afforded in practical engineering at present.

    “Although the objective function of satellite data transmission scheduling problem is an essential multiobjective optimization problem and MOEAs can obtain good results, the computational cost of MOEAs are much heavier than the algorithms belonging to the former two categories.This cost cannot be afforded in practical engineering at present.”

    From A Satellite Observation Data Transmission Scheduling Algorithm Oriented to Data Topics (2020) · cites this paper as [3]

Citations

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Flagged first, then by citations

Research works printed but not linked: 26

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  3. Yuqing, 2015, Satellite range scheduling with the priority constraint: an improved genetic algorithm using a station id encoding method, Chin. J. Aeronaut., 28, 789, 10.1016/j.cja.2015.04.012 · DOI
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  9. Vazquez, 2014, Resolution of an antenna–satellite assignment problem by means of integer linear programming, Aero. Sci. Technol., 39, 567, 10.1016/j.ast.2014.06.002 · DOI · Paper page
  10. Li, 2014, A data transmission scheduling algorithm for rapid-response earth-observing operations, Chin. J. Aeronaut., 27, 349, 10.1016/j.cja.2014.02.014 · DOI · Paper page
  11. Deb, 2000, A fast elitist multi-objective genetic algorithm: nsga-ii, IEEE Trans. Evol. Comput., 6, 182, 10.1109/4235.996017 · DOI · Paper page
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  13. Beumea, 2007, Sms-emoa: multiobjective selection based on dominated hypervolume, Eur. J. Oper. Res., 181, 1653, 10.1016/j.ejor.2006.08.008 · DOI
  14. Zhang, 2007, Moea/d: a multiobjective evolutionary algorithm based on decomposition, IEEE Trans. Evol. Comput., 11, 712, 10.1109/TEVC.2007.892759 · DOI · Paper page
  15. Li, 2014, Cooperative co-evolution with differential grouping for large scale optimization, IEEE Trans. Evol. Comput., 18, 378, 10.1109/TEVC.2013.2281543 · DOI
  16. Tan, 2006, A distributed cooperative coevolutionary algorithm for multiobjective optimization, IEEE Trans. Evol. Comput., 10, 527, 10.1109/TEVC.2005.860762 · DOI
  17. Zhang, 2007, Moea/d: a multiobjective evolutionary algorithm based on decomposition, IEEE Trans. Evol. Comput., 11, 712, 10.1109/TEVC.2007.892759 · DOI · Paper page
  18. Liu, 2014, Decomposition of a multiobjective optimization problem into a number of simple multiobjective subproblems, IEEE Trans. Evol. Comput., 18, 450, 10.1109/TEVC.2013.2281533 · DOI
  19. Bekele, 2007, Multi-objective automatic calibration of swat using nsga-ii, J. Hydrol., 341, 165, 10.1016/j.jhydrol.2007.05.014 · DOI
  20. Dhanalakshmi, 2011, Application of modified nsga-ii algorithm to combined economic and emission dispatch problem, Int. J. Electr. Power Energy Syst., 33, 992, 10.1016/j.ijepes.2011.01.014 · DOI
  21. Ramesh, 2012, Application of modified nsga-ii algorithm to multi-objective reactive power planning, Appl. Soft Comput., 12, 741, 10.1016/j.asoc.2011.09.015 · DOI
  22. Schuldt, 2004, Recognizing human actions: a local svm approach, vol. 3, 32
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  25. Zitzler, 2004, Indicator-based selection in multiobjective search, Lect. Notes Comput. Sci., 3242, 832, 10.1007/978-3-540-30217-9_84 · DOI
  26. Qi, 2016, Self-adaptive multi-objective evolutionary algorithm based on decomposition for large-scale problems: a case study on reservoir flood control operation, Inf. Sci. Int. J., 367–368, 529

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Metadata

Authors
Zhang, J., Xing, L., Peng, G., Yao, F., & Chen, C.
Affiliations
  • National University of Defense Technology (CN)
  • KU Leuven (BE)
Corresponding author
Xing, Lining
ORCID
Venue
Swarm and Evolutionary Computation
Publisher
Elsevier BV
Published
2019-11
Volume
50
Pages
100560
ISSN
2210-6502 (ISSN-L)
DOI
10.1016/j.swevo.2019.100560
OpenAlex
W2968726189
Cited by
48
References
1
Citations by year
2026: 8 · 2025: 7 · 2024: 7 · 2023: 6 · 2022: 8 · 2021: 8 · 2020: 4
FWCI
3.40
Type
journal article
Language
en
Open access
closed
Licence
Locations
Swarm and Evolutionary Computation (publishedVersion)
Funders
  • National Natural Science Foundation of China (61603400, 61773120, 61873328)
  • Foundation for the Author of National Excellent Doctoral Dissertation of the People's Republic of China (2014-92)
  • National Science Fund for Distinguished Young Scholars (61525304)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Multi-Objective Optimization Algorithms
  • Physical Sciences › Engineering › Aerospace Engineering › Satellite Communication Systems
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Scheduling and Optimization Algorithms
Keywords
multi-objective optimization, support vector machine, NSGA-II, large-scale optimization, resource conflicts, multi-objective evolutionary algorithms

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