The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Monarch butterfly optimization
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Citations
Cites
Genetic algorithms in search, optimization, and machine learning
(1989) · Choice Reviews Online · doi:10.5860/choice.27-0936 · Cited by 49,010
Storn, R., & Price, K. (1997) · Journal of Global Optimization · doi:10.1023/a:1008202821328 · Cited by 29,569
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2013.12.007 · Cited by 19,343
Genetic Algorithms in Search, Optimization and Machine Learning
Goldberg, D. E., & Robson, D. (1988) · Cited by 18,391
Genetic Programming: On the Programming of Computers by Means of Natural Selection
Koza, J. R. (1992) · Medical Entomology and Zoology · Cited by 13,082
Ant system: optimization by a colony of cooperating agents
Dorigo, M., Maniezzo, V., & Colorni, A. (1996) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/3477.484436 · Cited by 12,067
Karaboga, D., & Basturk, B. (2007) · Journal of Global Optimization · doi:10.1007/s10898-007-9149-x · Cited by 7,651
Cuckoo Search via Lévy flights
Yang, X.-S., & Deb, S. (2009) · 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC) · doi:10.1109/nabic.2009.5393690 · Cited by 6,185
Self-citationBäck, T. (1996) · Oxford University Press eBooks · doi:10.1093/oso/9780195099713.001.0001 · Cited by 5,113
Nature-Inspired Metaheuristic Algorithms
Yang, X. (2008) · Medical Entomology and Zoology · Cited by 4,556
Cited by
Political Optimizer: A novel socio-inspired meta-heuristic for global optimization
Askari, Q., Younas, I., & Saeed, M. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105709 · Cited by 615
Improving Metaheuristic Algorithms With Information Feedback Models
Wang, G.-G., & Tan, Y. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2780274 · Cited by 285
Self-citationAn efficient hybrid multilayer perceptron neural network with grasshopper optimization
Heidari, A. A., Faris, H., Aljarah, I., & Mirjalili, S. (2019) · Soft Computing · doi:10.1007/s00500-018-3424-2 · Cited by 283
Nature Inspired Methods and Their Industry Applications—Swarm Intelligence Algorithms
Slowik, A., & Kwasnicka, H. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2786782 · Cited by 257
Du, Y., Rafferty, A. R., McAuliffe, F. M., Wei, L., & Mooney, C. (2022) · Scientific Reports · doi:10.1038/s41598-022-05112-2 · Cited by 128
“We recognise that the performance of our models may require further improvement for use in a clinical setting, for example, by using some intelligent optimization algorithms 40–44 .”
Method
Metadata
- Authors
- Wang, G.-G., Deb, S., & Cui, Z.
- Venue
- Neural Computing and Applications
- Published
- 2015-05-19
- Volume
- 31
- Issue
- 7
- Pages
- 1995-2014
- ISSN
- 0941-0643 (ISSN-L)
- 1433-3058
- OpenAlex
- W341879454
- Cited by
- 983
- References
- 39
- FWCI
- 37.54
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Monarch butterfly optimization. https://app.cochat.ai/papers/10.1007/s00521-015-1923-y
- The paper
- Wang, G.-G., Deb, S., & Cui, Z. (2019). Monarch butterfly optimization. Neural Computing and Applications. https://doi.org/10.1007/s00521-015-1923-y
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.
