The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
A survey on optimization metaheuristics
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Citations
Cites
Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
Storn, R., & Price, K. (1997) · Journal of Global Optimization · doi:10.1023/a:1008202821328 · Cited by 29,569
A new optimizer using particle swarm theory
Eberhart, R., & Kennedy, J. (2002) · MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science · doi:10.1109/mhs.1995.494215 · Cited by 16,067
No free lunch theorems for optimization
Wolpert, D. H., & Macready, W. G. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585893 · Cited by 14,237
Differential Evolution: A Survey of the State-of-the-Art
Das, S., & Suganthan, P. N. (2011) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2010.2059031 · Cited by 5,307
Completely Derandomized Self-Adaptation in Evolution Strategies
Hansen, N., & Ostermeier, A. (2001) · Evolutionary Computation · doi:10.1162/106365601750190398 · Cited by 4,397
Glover, F., & Laguna, M. (1997) · doi:10.1007/978-1-4615-6089-0 · Cited by 3,001
An introduction and survey of estimation of distribution algorithms
Hauschild, M., & Pelikan, M. (2011) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2011.08.003 · Cited by 478
Cited by
Coyote optimization algorithm: A new metaheuristic for global optimization problems
Pierezan, J., & Dos Santos Coelho, L. (2018) · 2018 IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2018.8477769 · Cited by 645
Let a biogeography-based optimizer train your Multi-Layer Perceptron
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014) · Information Sciences · doi:10.1016/j.ins.2014.01.038 · Cited by 329
Benchmarking in Optimization: Best Practice and Open Issues
Bartz-Beielstein, T., Doerr, C., Berg, D. V. D., Bossek, J., Chandrasekaran, S., Eftimov, T., Fischbach, A., Kerschke, P., La Cava, W., Lopez-Ibanez, M., et al. (2020) · arXiv · doi:10.48550/arxiv.2007.03488 · Cited by 214
Simulated Annealing Algorithm for Deep Learning
Rere, L. M. R., Fanany, M. I., & Arymurthy, A. M. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.12.114 · Cited by 192
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
- Boussaïd, I., Lepagnot, J., & Siarry, P.
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2013-07
- Volume
- 237
- Pages
- 82-117
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W2118044993
- Cited by
- 1,655
- References
- 8
- FWCI
- 91.29
- Type
- journal article
- Language
- en
- Open access
- closed
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- Boussaïd, I., Lepagnot, J., & Siarry, P. (2013). A survey on optimization metaheuristics. Information Sciences. https://doi.org/10.1016/j.ins.2013.02.041
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.
