The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Slime mould algorithm: A new method for stochastic optimization
Open accessAbstract
Abstract In this paper, a new stochastic optimizer, which is called slime mould algorithm (SMA), is proposed based on the oscillation mode of slime mould in nature. The proposed SMA has several new features with a unique mathematical model that uses adaptive weights to simulate the process of producing positive and negative feedback of the propagation wave of slime mould based on bio-oscillator to form the optimal path for connecting food with excellent exploratory ability and exploitation propensity. The proposed SMA is compared with up-to-date metaheuristics using an extensive set of benchmarks to verify its efficiency. Moreover, four classical engineering problems are utilized to estimate the efficacy of the algorithm in optimizing constrained problems. The results demonstrate that the proposed SMA benefits from competitive, often outstanding performance on different search landscapes. The source codes of SMA are publicly available at http://www.alimirjalili.com/SMA.html and https://tinyurl.com/Slime-mould-algorithm .
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Storn, R., & Price, K. (1997) · Journal of Global Optimization · doi:10.1023/a:1008202821328 · Cited by 29,569
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
Hansen, N., Müller, S. D., & Koumoutsakos, P. (2003) · Evolutionary Computation · doi:10.1162/106365603321828970 · Cited by 2,601
Handbook of Parametric and Nonparametric Statistical Procedures: Third Edition
Sheskin, D. J. (2003) · doi:10.1201/9781420036268 · Cited by 1,551
Cited by
Qiu, S., Zhao, H., Jiang, N., Wang, Z., Liu, L., An, Y., Zhao, H., Miao, X., Liu, R., & Fortino, G. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.11.006 · Cited by 671
Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm
Oyelade, O. N., Ezugwu, A. E.-S., Mohamed, T. I. A., & Abualigah, L. (2022) · IEEE Access · doi:10.1109/access.2022.3147821 · Cited by 445
Heap-based optimizer inspired by corporate rank hierarchy for global optimization
Askari, Q., Saeed, M., & Younas, I. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113702 · Cited by 380
Chen, H., Heidari, A. A., Chen, H., Wang, M., Pan, Z., & Gandomi, A. H. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.04.008 · Cited by 322
Self-citationDu, Y., Rafferty, A. R., McAuliffe, F. M., Wei, L., & Mooney, C. (2022) · Scientific Reports · doi:10.1038/s41598-022-05112-2 · Cited by 128
“Our models included a very small set of features (4 or 5) compared with published models (4-2355), which means our models would be easier and faster to use.”
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Metadata
- Authors
- Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S.
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 111
- Pages
- 300-323
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W3014974411
- Cited by
- 3,036
- References
- 4
- FWCI
- 160.06
- Type
- journal article
- Language
- en
- Open access
- green
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