The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Whale optimization algorithm: a systematic review of contemporary applications, modifications and developments
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Citations
Cites
1 retracted
Abdel-Basset, M., Manogaran, G., El-Shahat, D., & Mirjalili, S. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.03.020 · Cited by 262
RetractedA 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
Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009) · PLoS Medicine · doi:10.1371/journal.pmed.1000100 · Cited by 28,164
The Whale Optimization Algorithm
Mirjalili, S., & Lewis, A. (2016) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2016.01.008 · Cited by 14,581
Karaboga, D., & Basturk, B. (2007) · Journal of Global Optimization · doi:10.1007/s10898-007-9149-x · Cited by 7,651
Empirical study of particle swarm optimization
Shi, Y., & Eberhart, R. C. (2003) · Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406) · doi:10.1109/cec.1999.785511 · Cited by 3,945
Ant colony optimization: a new meta-heuristic
Dorigo, M., & Di Caro, G. (2003) · Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406) · doi:10.1109/cec.1999.782657 · Cited by 2,137
A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example
Pan, W.-T. (2012) · Knowledge-Based Systems · doi:10.1016/j.knosys.2011.07.001 · Cited by 1,543
Knowles, J., & Corne, D. (2003) · Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406) · doi:10.1109/cec.1999.781913 · Cited by 1,278
Hybrid Whale Optimization Algorithm with simulated annealing for feature selection
Mafarja, M. M., & Mirjalili, S. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.04.053 · Cited by 1,148
Cited by
Applications and Challenges of Artificial Intelligence in Space Missions
Oche, P. A., Ewa, G. A., & Ibekwe, N. (2024) · IEEE Access · doi:10.1109/access.2021.3132500 · Cited by 68
“Ashraf in [55] noted nine recently developed optimization algorithms inspired by the biological behavior of some animals when fighting for food and mates: a. Genetic Bee Colony (GBC) algorithm [56] is a new optimization algorithm that integrates the advantages of the Genetic Algorithms (GA) and Artificial Bee Colony (…”
Influential
Metadata
- Authors
- Rana, N., Latiff, M. S. A., Abdulhamid, S. M., & Chiroma, H.
- Venue
- Neural Computing and Applications
- Published
- 2020-03-30
- Volume
- 32
- Issue
- 20
- Pages
- 16245-16277
- ISSN
- 0941-0643 (ISSN-L)
- 1433-3058
- OpenAlex
- W3014628627
- Cited by
- 357
- References
- 60
- FWCI
- 4.57
- Type
- journal article
- Language
- en
- Open access
- closed
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- Rana, N., Latiff, M. S. A., Abdulhamid, S. M., & Chiroma, H. (2020). Whale optimization algorithm: a systematic review of contemporary applications, modifications and developments. Neural Computing and Applications. https://doi.org/10.1007/s00521-020-04849-z
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.
