Swarm intelligence: from natural to artificial systems
Keywords:
swarm intelligence, optimization, Artificial intelligenceAbstract
Artificial intelligence (ai) is no longer science fiction and is already commonplace in our lives. The different areas of the AI imitate what happens in nature, an example is the behavior of different gregarious animals, such as bees or wolves. The behavior of these social structures is used to find solutions to different problems. This article presents some examples of algorithms that imitate the behavior of different animals and their application in daily life, within the framework of a discipline known as swarm intelligence.
References
Bayar, N., Darmoul, S., Hajri-Gabouj, S. y Pierreval, H. (2015). Fault detection, diagnosis and recovery using artificial immune systems: A review. Engineering Applications of Artificial Intelligence, 46(A), 43-57. https://doi.org/10.1016/j.engappai.2015.08.006
Chaudhary, R. y Banati, H. (2019). Swarm bat algorithm with improved search (SBAIS). Soft Computing, 23, 11461-11491. https://doi.org/10.1007/s00500-018-03688-4
Delvalle-Arroyo, P.E., Fory-Aguirre, C.A. y Serna-Ramírez, J.M. (2015). Cellular automata: Control improvements and immunity in the simulation of propagative phenomena. Sistemas y Telemática, 13(35), 9-22.
Han, J.H., Lee, J.S. y Kim, D.K. (2009). Bio-inspired flapping UAV design: A university perspective. En las memorias del SPIE, 7295, Health Monitoring of Structural and Biological Systems, 72951l. https://doi.org/10.1117/12.815337
Ileri, E., Karaolgan, A. D. y Akpinar, S. (2020). Optimizing cetane improver concentration in biodiesel-diesel blend via grey wolf optimizer algorithm. Fuel, 273, 117784. https://doi.org/10.1016/j.fuel.2020.117784
Kim, H., Kim, J. y Jung H. (2018). Convolutional neural network based image processing system. Journal of Information and communication Convergence Engineering, 16(3), 160-165. https://doi.org/10.6109/jicce.2018.16.3.160
Kumar, A., Kumar, D. y Jarial, S.K. (2016). A comparative analysis of selection schemes in the artificial bee colony algorithm. Computación y Sistemas, 20(1), 55-66. https://doi.org/10.13053/cys-20-1-2228
Lobato, F.S. y Steffen, V. (2014). Fish swarm optimization algorithm applied to engineering system design. Latin American Journal of Solids and Structures, 11(1), 143-156. https://doi.org/10.1590/S1679-78252014000100009
Mandloi, M. y Bhatia, V. (2017). Capítulo 12 – Symbol detection in multiple antenna wireless system via ant colony optimization. Handbook of Neural Computation, Academic Press, 225-237. https://doi.org/10.1016/B978-0-12-811318-9.00012-0
Moosavi, S. H. y Bardsiri. V. K. (2017). Satin bowerbird optimizer: A new optimization algorithm to optimize ANFIS for software development effort estimation. Engineering Applications of Artificial Intelligence, 60, 1-15. https://doi.org/10.1016/j.engappai.2017.01.006
Obando-Paredes, E.D. (2017). Algoritmos genéticos y PSO aplicados a un problema de generación distribuida. Scientia et Technica, 22(1), 15-23.
Valdez F., Melin P., Castillo O. (2014). A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation. Expert Systems with Applications, 41(14), 6459-6466. https://doi.org/10.1016/j.eswa.2014.04.015
Wu, T.Q., Yao, M. y Yang, J.H. (2016). Dolphin swarm algorithm . Frontiers of Information Technology & Electronic Engeneering, 17, 717-729. https://doi.org/10.1631/FITEE.1500287
Xie, L., Han, T., Zhou, H., Zhang, Z. R., Han, B. y Tang, A. (2021). Tuna swarm optimization: A novel swarm-based metaheuristic algorithm for global optimization. Artificial Intelligence and Machine Learning-Driven Decision-Making, 2021. https://doi.org/10.1155/2021/9210050
Yang, X.S. (2021). Capítulo 9. Firefly algorithms. Nature-Inspired Optimization Algorithms. 2da ed., Academic Press. 123-139.
Yilmaz, S. y Sen, S. (2020). Electric fish optimization: a new heuristic algorithm inspired by electrolocation. Neural Computing and Applications, 32, 11543-11578. https://doi.org/10.1007/s00521-019-04641-8
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