The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Optimization based on bacterial chemotaxis
Abstract
We present an optimization algorithm based on a model of bacterial chemotaxis. The original biological model is used to formulate a simple optimization algorithm, which is evaluated on a set of standard test problems. Based on this evaluation, several features are added to the basic algorithm using evolutionary concepts in order to obtain an improved optimization strategy, called the bacteria chemotaxis (BC) algorithm. This strategy is evaluated on a number of test functions for local and global optimization, compared with other optimization techniques, and applied to the problem of inverse airfoil design. The comparisons show that on average, BC performs similar to standard evolution strategies and worse than evolution strategies with enhanced convergence properties.
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Cited by
Rajwar, K., Deep, K., & Das, S. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10470-y · Cited by 687
Krishnanand, K. N., & Ghose, D. (2009) · Swarm Intelligence · doi:10.1007/s11721-008-0021-5 · Cited by 447
Houssein, E. H., Gad, A. G., Wazery, Y. M., & Suganthan, P. N. (2021) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2021.100841 · Cited by 355
Chemotaxis emerges as the optimal solution to cooperative search games
Pezzotta, A., Adorisio, M., & Celani, A. (2018) · Physical Review E · doi:10.1103/physreve.98.042401 · Cited by 17
Metadata
- Authors
- Muller, S. D., Marchetto, J., Airaghi, S., & Kournoutsakos, P.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2002
- Volume
- 6
- Issue
- 1
- Pages
- 16-29
- ISSN
- 1089-778X (ISSN-L)
- OpenAlex
- W2141916469
- Cited by
- 329
- References
- 14
- FWCI
- 5.02
- Type
- journal article
- Language
- en
- Open access
- closed
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