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The power of intelligence emerging from swarms

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Języki publikacji
EN
Abstrakty
EN
Swarm intelligence (SI) is a field of study that seeks to understand and modelcollective behaviors observed in natural social systems, such as ant colonies, beehives, termite mounds, flocks of birds or schools of fish. The central principleof SI is that complex intelligent behaviors can emerge from the interactions of large numbers of simple individual entities, without any centralized controlor monitoring. SI researchers aim to uncover the underlying principles and mechanisms behind this SI, with the aim of applying these concepts to solve complex problems in areas such as optimization, robotics, transport, IT, etc. As the field continues to evolve, SI is expected to have an increasingly significant impact on our understanding of biological systems and our ability to design intelligent systems capable of adapting and thriving in complex environments and dynamic. This article aims to introduce the reader to the field of SI, presenting its fundamental concepts, key principles, existing applications, and prospective future developments.
Wydawca
Czasopismo
Rocznik
Tom
Strony
77--100
Opis fizyczny
Bibliogr. 44 poz., rys., tab., wykr.
Twórcy
  • Ibn Zohr University, Faculty of Sciences, Department of Computer Science, Agadir, Morocco
Bibliografia
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  • [4] Adrdor R., Koutti L.: Asynchronous Forward-Bounding algorithm with Directional Arc Consistency. In: ASPOCP 2021: Workshop on Answer Set Programming and Other Computing Paradigms 2021 co-located with ICLP 2021 Porto, Portugal, September 21, 2021. https://ceur-ws.org/Vol-2970/aspocppaper7.pdf.
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  • [7] Alrefai N., Ibrahim O.: Optimized feature selection method using particle swarm intelligence with ensemble learning for cancer classification based on microarraydatasets, Neural Computing and Applications, vol. 34(16), pp. 13513–13528, 2022.doi: 10.1007/s00521-022-07147-y.
  • [8] Altshuler Y.: Recent Developments in the Theory and Applicability of Swarm Search, Entropy, vol. 25(5), 710, 2023. doi: 10.3390/e25050710.
  • [9] Altshuler Y., Pentland A., Bruckstein A.M.:Swarms and network intelligence insearch, Springer, 2018. doi: 10.1007/978-3-319-63604-7.
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  • [12] Chen Z., Liu J., Wang Y.: Big Data Swarm Intelligence Optimization Algorithm Application in the Intelligent Management of an E-Commerce Logistics Ware-house, Journal of Cases on Information Technology, vol. 26(1), pp. 1–19, 2024.
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  • [17] Drias H., Drias Y., Houacine N.A., Bendimerad L.S., Zouache D., Khen-nak I.: Quantum OPTICS and deep self-learning on swarm intelligence algorithms for Covid-19 emergency transportation, Soft Computing, vol. 27(18),pp. 13181–13200, 2023.
  • [18] Efremov M.A., Kholod I.I.: Swarm robotics foraging approaches. In: 2020 IEEE conference of Russian young researchers in electrical and electronic engineering (EIConRus), pp. 299–304, IEEE, 2020. doi: 10.1109/eiconrus49466.2020.9039340.
  • [19] Fidanova S.: Ant colony optimization. In: Ant Colony Optimization and Applications, Studies in Computational Intelligence, vol. 947, pp. 3–8, Springer, Cham, 2021. doi: 10.1007/978-3-030-67380-22.
  • [20] Figueiredo E., Macedo M., Siqueira H.V., Santana Jr C.J., Gokhale A., Bastos-Filho C.J.A.: Swarm intelligence for clustering : A systematic review with new perspectives on data mining, Engineering Applications of Artificial Intelligence, vol. 82, pp. 313–329, 2019. doi: 10.1016/j.engappai.2019.04.007.
  • [21] Gonzalez-Santos C., Vega-Rodr ıguez M.A., P erez C.J.: Addressing topic modeling with a multi-objective optimization approach based on swarm intelligence, Knowledge-Based Systems, vol. 225, 107113, 2021. doi: 10.1016 /j.knosys.2021.107113.
  • [22] Guo C., Tang H., Niu B., Lee C.B.P.: A survey of bacterial for aging optimization, Neurocomputing, vol. 452, pp. 728–746, 2021. doi: 10.1016/j.neucom.2020.06.142.
  • [23] Hasegawa K., Noto M.: Swarm intelligence algorithm for optimality discovery indistributed constraint optimization. In:2014 IEEE International Conference onSystems, Man, and Cybernetics (SMC), pp. 3611–3616, IEEE, 2014. doi: 10.1109/smc.2014.6974490.
  • [24] Hassan K.M., Abdo A., Yakoub A.: Enhancement of health care services basedon cloud computing in IoT environment using hybrid swarm intelligence, IEEE Access, vol. 10, pp. 105877–105886, 2022. doi: 10.1109/access.2022.3211512.
  • [25] Hou K., Yang Y., Yang X., Lai J.: Cooperative control and communicationof intelligent swarms: A survey, Control Theory and Technology, vol. 18(2),pp. 114–134, 2020. doi: 10.1007/s11768-020-9195-1.
  • [26] Hu J., Wu H., Zhong B., Xiao R.: Swarm intelligence-based optimisation algorithms: An overview and future research issues, International Journal of Automation and Control, vol. 14(5-6), pp. 656–693, 2020. doi: 10.1504/ijaac.2020.10030986.
  • [27] Kwa H.L., Babineau V., Philippot J., Bouffanais R.: Adapting the Exploration–Exploitation Balance in Heterogeneous Swarms: Tracking Evasive Targets, Artificial Life, vol. 29(1), 2022. doi: 10.1162/artla00390.
  • [28] Li X., Shu Z.: Research on Big Data Text Clustering Algorithm Based on SwarmIntelligence, Wireless Communications and Mobile Computing, vol. 2022(1), 7551035, 2022. doi: 10.1155/2022/7551035.
  • [29] Lin J.H., Yeh M.C.: A Swarm Intelligence Approach to Parameters Identification of Chaotic Systems. In: 2006 IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3509–3514, IEEE, 2006. doi: 10.1109/icsmc.2006.384663.
  • [30] Makhadmeh S.N., Al-Betar M.A., Abasi A.K., Awadallah M.A., Doush I.A.,Alyasseri Z.A.A., Alomari O.A.: Recent advances in butterfly optimization algorithm, its versions and applications, Archives of Computational Methods in Engineering, vol. 30(2), pp. 1399–1420, 2023. doi: 10.1007/s11831-022-09843-3.
  • [31] Makhadmeh S.N., Al-Betar M.A., Doush I.A., Awadallah M.A., Kassaymeh S., Mirjalili S., Zitar R.A.: Recent advances in Grey Wolf Optimizer, its versionsand applications, IEEE Access, vol. 12, pp. 22991–23028, 2023. doi: 10.1109/ACCESS.2023.3304889.
  • [32] Millonas M.M.: Swarms, phase transitions, and collective intelligence, arXivpreprint adap-org/9306002, 1993.
  • [33] Nedjah N., Mourelle L.d.M., Lizarazu M.S.D.: Swarm Intelligence-Based Multi-Objective Optimization Applied to Industrial Cooling Towers for Energy Efficiency, Sustainability, vol. 14(19), 11881, 2022. doi: 10.3390/su141911881.
  • [34] Neme A., Hernandez S.: Algorithms inspired in social phenomena. In:Nature-inspired algorithms for optimisation, pp. 369–387, Springer, 2009. doi: 10.1007/978-3-642-00267-013.
  • [35] Pourpanah F., Wang R., Lim C.P., Wang X.Z., Yazdani D.: A review of artificialfish swarm algorithms: Recent advances and applications, Artificial Intelligence Review, vol. 56(3), pp. 1867–1903, 2023. doi: 10.1007/s10462-022-10214-4.
  • [36] Pratiwi L., Choo Y.H., Muda A.K., Pratama S.F.: Swarm Intelligence-basedHierarchical Clustering for Identification of ncRNA using Covariance Search Model., International Journal of Advanced Computer Science and Applications, vol. 13(11), pp. 822–831, 2022. doi: 10.14569/ijacsa.2022.0131195.
  • [37] Reynolds C.W.: Flocks, herds and schools: A distributed behavioral model. In: Proceedings of the 14th annual conference on Computer graphics and interactive techniques, pp. 25–34, 1987. doi: 10.1145/280811.281008.
  • [38] Sasaki H.: Emulating heterogeneity of individuals and visualizing its influence onant swarm migration, Applied Artificial Intelligence, vol. 36(1), 2138120, 2022.doi: 10.1080/08839514.2022.2138120.
  • [39] Shahzad M.M., Saeed Z., Akhtar A., Munawar H., Yousaf M.H., Baloach N.K., Hussain F.: A review of swarm robotics in a nutshell, Drones, vol. 7(4), 269,2023. doi: 10.3390/drones7040269.
  • [40] Wilson J., Chance G., Winter P., Lee S., Milner E., Abeywickrama D., Windsor S.,et al.: Trustworthy Swarms. In:TAS ’23: Proceedings of the First International Symposium on Trustworthy Autonomous Systems, 2023. doi: 10.1145/3597512.3599705.
  • [41] Yang X.S., Karamanoglu M.: Swarm intelligence and bio-inspired computation: an overview, Swarm Intelligence and Bio-Inspired Computation, pp. 3–23, 2013. doi: 10.1016/B978-0-12-405163-8.00001-6.
  • [42] Youssefi K.A.R., Rouhani M.: Swarm intelligence based robotic search in unknown maze-like environments, Expert Systems with Applications, vol. 178,114907, 2021. doi: 10.1016/j.eswa.2021.114907.
  • [43] Yu Z., Li C., Zhou J.: Tunnel Boring Machine Performance Prediction Using Supervised Learning Method and Swarm Intelligence Algorithm, Mathematics, vol. 11(20), 4237, 2023. doi: 10.3390/math11204237.
  • [44] Zhang X., Wei Y., Hashim Z.: Improvement of Swarm Intelligence Algorithm and Its Application in Logistics Network Routing, Journal of Network Intelligence, vol. 8(4), pp. 1077–1094, 2023. https://bit.kuas.edu.tw/∼jni/2023/vol8/s4/02.JNI-S-2023-04-014.pdf.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-46b20e07-faca-4d90-aed8-f90955c5637a
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