The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
A hybrid multi-objective Particle Swarm Optimization for scientific workflow scheduling
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Cites
Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
Multi-Objective Optimization Using Evolutionary Algorithms
Deb, K., & Kalyanmoy, D. (2001) · Andalas University Repository (Andalas University) · Cited by 14,918
Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A. F., & Buyya, R. (2011) · Software: Practice and Experience · doi:10.1002/spe.995 · Cited by 4,948
Handling multiple objectives with particle swarm optimization
Coello, C. A. C., Pulido, G. T., & Lechuga, M. S. (2004) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2004.826067 · Cited by 4,455
Performance-effective and low-complexity task scheduling for heterogeneous computing
Topcuoglu, H., Hariri, S., & Wu, M.-Y. (2002) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/71.993206 · Cited by 3,475
Cloud Computing and Grid Computing 360-Degree Compared
Foster, I., Zhao, Y., Raicu, I., & Lu, S. (2008) · 2008 Grid Computing Environments Workshop · doi:10.1109/gce.2008.4738445 · Cited by 2,912
Multiobjective evolutionary algorithms: A survey of the state of the art
Zhou, A., Qu, B.-Y., Li, H., Zhao, S.-Z., Suganthan, P. N., & Zhang, Q. (2011) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2011.03.001 · Cited by 2,255
A Taxonomy of Workflow Management Systems for Grid Computing
Yu, J., & Buyya, R. (2005) · Journal of Grid Computing · doi:10.1007/s10723-005-9010-8 · Cited by 834
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Cited by
MPSO: Modified particle swarm optimization and its applications
Tian, D., & Shi, Z. (2018) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2018.01.011 · Cited by 363
Singh, H., Tyagi, S., Kumar, P., Gill, S. S., & Buyya, R. (2021) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2021.102353 · Cited by 104
Metadata
- Authors
- Verma, A., & Kaushal, S.
- Venue
- Parallel Computing
- Publisher
- Elsevier BV
- Published
- 2017-02
- Volume
- 62
- Pages
- 1-19
- ISSN
- 0167-8191 (ISSN-L)
- OpenAlex
- W2581327637
- Cited by
- 258
- References
- 25
- FWCI
- 48.90
- Type
- journal article
- Language
- en
- Open access
- closed
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- Verma, A., & Kaushal, S. (2017). A hybrid multi-objective Particle Swarm Optimization for scientific workflow scheduling. Parallel Computing. https://doi.org/10.1016/j.parco.2017.01.002
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.
