The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
Nature Inspired Methods and Their Industry Applications—Swarm Intelligence Algorithms
Abstract
In this paper, we present the swarm intelligence (SI) concept and mention some metaheuristics belonging to the SI. We present the particle swarm optimization (PSO) algorithm and the ant colony optimization (ACO) method as the representatives of the SI approach. In recent years, researchers are eager to develop and apply a variety of these two methods, despite the development of many other newer methods as Bat or FireFly algorithms. Presenting the PSO and ACO we put their pseudocode, their properties, and intuition lying behind them. Next, we focus on their real-life applications, indicating many papers presented varieties of basic algorithms and the areas of their applications.
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Cites
Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192
Genetic algorithms in search, optimization, and machine learning
(1989) · Choice Reviews Online · doi:10.5860/choice.27-0936 · Cited by 49,010
Particle swarm optimization: An overview
Poli, R., Kennedy, J., & Blackwell, T. (2007) · Swarm Intelligence · doi:10.1007/s11721-007-0002-0 · Cited by 21,101
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
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
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
A New Metaheuristic Bat-Inspired Algorithm
Yang, X.-S. (2010) · Studies in Computational Intelligence · doi:10.1007/978-3-642-12538-6_6 · Cited by 4,905
Firefly Algorithms for Multimodal Optimization
Yang, X.-S. (2009) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-04944-6_14 · Cited by 4,075
Cited by
Emperor penguin optimizer: A bio-inspired algorithm for engineering problems
Dhiman, G., & Kumar, V. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.06.001 · Cited by 720
Shaikh, P. W., El-Abd, M., Khanafer, M., & Gao, K. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3014296 · Cited by 183
A survey on collaborative hunting with robotic swarm: Key technologies and application scenarios
Cai, W., Chen, H., & Zhang, M. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128008 · Cited by 20
Metadata
- Authors
- Slowik, A., & Kwasnicka, H.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2018-03
- Volume
- 14
- Issue
- 3
- Pages
- 1004-1015
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2777482433
- Cited by
- 257
- References
- 69
- FWCI
- 13.45
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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