Relevant bibliographies by topics / Swarm Intelligence

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Swarm Intelligence'

Author: Grafiati
Published: 4 June 2021
Last updated: 30 July 2024

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Journal articles on the topic "Swarm Intelligence"

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Hinchey, Michael G., Roy Sterritt, and Chris Rouff. "Swarms and Swarm Intelligence." Computer 40, no. 4 (April 2007): 111–13. http://dx.doi.org/10.1109/mc.2007.144.

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Vehlken, Sebastian. "Pervasive Intelligence." Digital Culture & Society 4, no. 1 (March 1, 2018): 107–32. http://dx.doi.org/10.14361/dcs-2018-0108.

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Abstract This article seeks to situate collective or swarm robotics (SR) on a conceptual pane which on the one hand sheds light on the peculiar form of AI which is at play in such systems, whilst on the other hand it considers possible consequences of a widespread use of SR with a focus on swarms of Unmanned Aerial Systems (Swarm UAS). The leading hypothesis of this article is that Swarm Robotics create a multifold “spatial intelligence”, ranging from the dynamic morphologies of such collectives via their robust self-organization in changing environments to representations of these environments as distributed 4D-sensor systems. As is shown on the basis of some generative examples from the field of UAS, robot swarms are imagined to literally penetrate space and control it. In contrast to classical forms of surveillance or even “sousveillance”, this procedure could be called perveillance.
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Issayeva, G. B. Issayeva, M. S. Ibraev, A. K. Koishybekova, B. R. Absatarova, A. A. Aitkazina, Sh P. Sh.P. Zhumagulova, N. Vodolazkina, and Z. M. Ibraeva. "SWARM INTELLIGENCE." EurasianUnionScientists 6, no. 8(77) (September 13, 2020): 9–13. http://dx.doi.org/10.31618/esu.2413-9335.2020.6.77.998.

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This report investigates this discipline that deals with natural and artificial systems. In the past few years there has been a lot of research on the application of swarm intelligence. A large number of algorithms have been used in different spheres of our life. In this paper we give an overview of this research area. We identify one of the algorithms of swarm intelligence systems and we show how it is used to solve problems. In other words, we present Bee Algorithms, a general framework in which most swarm intelligence algorithms can be placed. After that, we give an extensive solution of existing problem, discussing algorithm’s advantages and disadvantages. We conclude with an overview of future research directions that we consider important for the further development of this field.
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Wanka, Rolf. "Swarm intelligence." it - Information Technology 61, no. 4 (August 27, 2019): 157–58. http://dx.doi.org/10.1515/itit-2019-0034.

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Tarasewich, Peter, and Patrick R. McMullen. "Swarm intelligence." Communications of the ACM 45, no. 8 (August 2002): 62–67. http://dx.doi.org/10.1145/545151.545152.

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Dorigo, Marco, and Mauro Birattari. "Swarm intelligence." Scholarpedia 2, no. 9 (2007): 1462. http://dx.doi.org/10.4249/scholarpedia.1462.

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Shatkin, Max A. "The problem of privacy in a digital society of swarm intelligence." Izvestiya of Saratov University. Philosophy. Psychology. Pedagogy 24, no. 1 (March 21, 2024): 62–66. http://dx.doi.org/10.18500/1819-7671-2024-24-1-62-66.

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Introduction. The development of digital technologies requires the specification of the type of the emerging digital society. One of the development scenarios is the formation of a digital society of swarm intelligence. Theoretical analysis. The concept of swarm intelligence means an optimization algorithm that imitates the behavior of swarms or colonies of insects and bird flocks. Human society is characterized by the manifestation of swarm intelligence as a condition of joint organized activity, but the development of digital technologies brings swarm intelligence to a higher level. Creation of private swarms causes risks in the form of “Byzantine robot” – a hacked particle of the swarm, transmitting false information and jeopardizing the whole swarm. The solution is to incorporate elements of the economic game that support the values of honesty, cooperation and solidarity into the particle interactions. This leads to making privacy a “process” and changing its value status. Protecting privacy in swarm intelligence society has the potential to cause labor to lose its public status and acquire a private, hidden status and anonymize employment. Conclusion. The possible scenario of human society development towards the digital society of swarm intelligence can become a source of profound social changes and, above all, a rethinking of the social stratification based on the division of labor and social hierarchy in general.
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Semwal, Archit, Sadik Shikalgar, and Dr Ramesh Solanki. "The Use of Artificial Intelligence in Swarm Drones." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (July 31, 2023): 1052–57. http://dx.doi.org/10.22214/ijraset.2023.54799.

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Abstract: Swarm robotics, a field that draws inspiration from the collective behavior observed in natural swarms, has gained significant attention in recent years. Swarm drones, a specific subset of swarm robotics, involve the coordination and collaboration of multiple autonomous drones to accomplish complex tasks. With the advancements in artificial intelligence (AI) techniques, the integration of AI algorithms and approaches has revolutionized swarm drone systems. This research paper provides a comprehensive review of the use of AI in swarm drones, covering various aspects such as swarm formation, task allocation, navigation, communication, and decision-making. The paper discusses the current state of the art, challenges, and potential future directions in this exciting field
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Zangana, Hewa Majeed, Zina Bibo Sallow, Mohammed Hazim Alkawaz, and Marwan Omar. "Unveiling the Collective Wisdom: A Review of Swarm Intelligence in Problem Solving and Optimization." Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi 9, no. 2 (May 10, 2024): 101–10. http://dx.doi.org/10.25139/inform.v9i2.7934.

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Swarm intelligence, inspired by the collective behaviour of natural swarms and social insects, represents a powerful paradigm for solving complex optimization and decision-making problems. In this review paper, we provide an overview of swarm intelligence, covering its definition, principles, algorithms, applications, performance evaluation, challenges, and future directions. We discuss prominent swarm intelligence algorithms, such as ant colony optimization, particle swarm optimization, and artificial bee colony algorithm, highlighting their applications in optimization, robotics, data mining, telecommunications, and other domains. Furthermore, we examine the performance evaluation and comparative studies of swarm intelligence algorithms, emphasizing the importance of metrics, comparative analysis, and case studies in assessing algorithmic effectiveness and practical applicability. Challenges facing swarm intelligence research, such as scalability, robustness, and interpretability, are identified, and potential future directions for addressing these challenges and advancing the field are outlined. In conclusion, swarm intelligence offers a versatile and effective approach to solving a wide range of optimization and decision-making problems, with applications spanning diverse domains and industries. By addressing current challenges, exploring new research directions, and embracing interdisciplinary collaborations, swarm intelligence researchers can continue to innovate and develop cutting-edge algorithms with profound implications for science, engineering, and society.
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Ahmad Shaban, Awaz, Jayson A. Dela Fuente, Merdin Shamal Salih, and Resen Ismail Ali. "Review of Swarm Intelligence for Solving Symmetric Traveling Salesman Problem." Qubahan Academic Journal 3, no. 2 (July 27, 2023): 10–27. http://dx.doi.org/10.48161/qaj.v3n2a141.

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Swarm Intelligence algorithms are computational intelligence algorithms inspired from the collective behavior of real swarms such as ant colony, fish school, bee colony, bat swarm, and other swarms in the nature. Swarm Intelligence algorithms are used to obtain the optimal solution for NP-Hard problems that are strongly believed that their optimal solution cannot be found in an optimal bounded time. Travels Salesman Problem (TSP) is an NP-Hard problem in which a salesman wants to visit all cities and return to the start city in an optimal time. In this article we are applying most efficient heuristic based Swarm Intelligence algorithms which are Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Bat algorithm (BA), and Ant Colony Optimization (ACO) algorithm to find a best solution for TSP which is one of the most well-known NP-Hard problems in computational optimization. Results are given for different TSP problems comparing the best tours founds by BA, ABC, PSO and ACO.
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Dissertations / Theses on the topic "Swarm Intelligence"

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Lang, Andreas. "Face Detection using Swarm Intelligence." Universitätsbibliothek Chemnitz, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-64415.

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Groups of starlings can form impressive shapes as they travel northward together in the springtime. This is among a group of natural phenomena based on swarm behaviour. The research field of artificial intelligence in computer science, particularly the areas of robotics and image processing, has in recent decades given increasing attention to the underlying structures. The behaviour of these intelligent swarms has opened new approaches for face detection as well. G. Beni and J. Wang coined the term “swarm intelligence” to describe this type of group behaviour. In this context, intelligence describes the ability to solve complex problems. The objective of this project is to automatically find exactly one face on a photo or video material by means of swarm intelligence. The process developed for this purpose consists of a combination of various known structures, which are then adapted to the task of face detection. To illustrate the result, a 3D hat shape is placed on top of the face using an example application program.
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Keshtkar, Abolfazl. "Swarm intelligence-based image segmentation." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27525.

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One of the major difficulties met in image segmentation lies in the varying degrees of homogeneousness of the different regions in a given image. Hence, it is more efficient to adopt adaptive threshold type methodologies to identify the regions in the images. Throughout the last decade, many image processing tools and techniques have emerged based on the former technology which we called conventional and new technologies such as intelligent-based image processing techniques and algorithm. In some cases, a combination of both technologies is adapted to form a hybrid image processing technique. Intelligent-based techniques are increasing nowadays. Due to the rapid growth of agent-based technology's environments which are adopting numerous agent-based applications, tools, models and softwares to enhance and improve the quality of the agent based approach. In case of intelligent techniques to doing image processing; swarm intelligence techniques rarely have been used in term of image segmentation or boundary detection. However, there are many factors that make this task challenging. These factors include not only the limited such increasing number of agents in the environment, and the presence of techniques., but also how to efficiently find the right threshold in the image, develop a flexible design, and fully autonomous system that support different platform. A flexible architecture and tools need to be defined that overcomes these problems and permits a smooth and valuable image processing based on these new techniques in image processing. It would satisfy the needs of end users. This thesis illustrates the theoretical background, design, swarm based intelligent techniques and implementation of a fully agent-based model system that is called SIBIS (Swarm Intelligent Based Image Segmentation).
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Lang, Andreas. "Face Detection using Swarm Intelligence." Technische Universität Chemnitz, 2010. https://monarch.qucosa.de/id/qucosa%3A19439.

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Groups of starlings can form impressive shapes as they travel northward together in the springtime. This is among a group of natural phenomena based on swarm behaviour. The research field of artificial intelligence in computer science, particularly the areas of robotics and image processing, has in recent decades given increasing attention to the underlying structures. The behaviour of these intelligent swarms has opened new approaches for face detection as well. G. Beni and J. Wang coined the term “swarm intelligence” to describe this type of group behaviour. In this context, intelligence describes the ability to solve complex problems. The objective of this project is to automatically find exactly one face on a photo or video material by means of swarm intelligence. The process developed for this purpose consists of a combination of various known structures, which are then adapted to the task of face detection. To illustrate the result, a 3D hat shape is placed on top of the face using an example application program.:1 Introduction 1.1 Face Detection 1.2 Swarm Intelligence and Particle Swarm Optimisation Fundamentals 3 Face Detection by Means of Particle Swarm Optimisation 3.1 Swarms and Particles 3.2 Behaviour Patterns 3.2.1 Opportunism 3.2.2 Avoidance 3.2.3 Other Behaviour Patterns 3.3 Stop Criterion 3.4 Calculation of the Solution 3.5 Example Application 4 Summary and Outlook
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Tiboni, Ivan. "I principi della swarm intelligence." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/4051/.

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Pontellini, Lorenzo. "Applicazioni informatiche della Swarm Intelligence." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/4658/.

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Lo studio che si ha in informatica ha come obiettivo la scoperta di algoritmi sempre più efficienti per riuscire, con componenti semplici, a svolgere compiti complessi, con il minore carico di lavoro possibile. Le applicazioni di tale studio trovano risultati anche nel campo del controllo adattativo di robot. Si vogliono confrontare tramite questo studio le osservazioni più importanti riguardati queste caratteristiche rese note dalla scienza e applicarle ai campi sopra citati per dimostrare l'effettivo valore e affidabilità che si guadagnano andando a utilizzare degli algoritmi che rispecchiano le stesse caratteristiche che si possono notare nel regno animale. La metodologia di interesse usata come caso di studio è quella del recupero di oggetti. Esistono numerose soluzioni a questo problema che possono trovare uso in molte realtà utili all'uomo. Ne verranno presentate e confrontate due all'interno di questo elaborato, studiando le caratteristiche positive e negative di entrambe. Questi due approcci sono chiamati a soglia fissa e a soglia variabile. Entrambe sono tipologie di adattamento che prendono spunto dal comportamento che hanno le colonie di formiche quando si muovono alla ricerca di cibo. Si è deciso di analizzare queste due metodologie partendo da una panoramica generale di come cooperano gli insetti per arrivare al risultato finale, per poi introdurre nello specifico le caratteristiche di entrambe analizzando per ognuna i risultati ottenuti tramite grafici, e confrontandoli tra di loro.
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Ciani, Gabriele <1993&gt. "Portfolio Selection with Swarm Intelligence." Master's Degree Thesis, Università Ca' Foscari Venezia, 2018. http://hdl.handle.net/10579/12769.

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The introduction of budget, cardinality and composition constraints to the portfolio selection problem implies the utilization of modern techniques for the achievement of the solution. In particular, this thesis will analyse Particle Swarm Optimization, a bio-inspired metaheuristic algorithm that aims to explore the search space in order to find optimal solutions. The problem considered consists in the minimization of a coherent risk measure, the expected shortfall, subject to risk adjusted performance constraints, budget, cardinality and fractions constraints. In practice, a chosen number of particles are exploring the set of feasible solutions. To the position of each particle is assigned a value of the objective function which accounts for the risk measure and for penalties associated to the constraints. Particles move according to signals given by their neighbors, by the particle with the best result and by their own memory. The implementation of the PSO algorithm is used to find a feasible and well diversified portfolio composed by Exchange Traded Funds sold on the Italian market, Borsa Italiana.
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FARMANI, MOHAMMAD REZA. "Clustering analysis using Swarm Intelligence." Doctoral thesis, Università degli Studi di Cagliari, 2016. http://hdl.handle.net/11584/266871.

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This thesis is concerned with the application of the swarm intelligence methods in clustering analysis of datasets. The main objectives of the thesis are ∙ Take the advantage of a novel evolutionary algorithm, called artificial bee colony, to improve the capability of K-means in finding global optimum clusters in nonlinear partitional clustering problems. ∙ Consider partitional clustering as an optimization problem and an improved antbased algorithm, named Opposition-Based API (after the name of Pachycondyla APIcalis ants), to automatic grouping of large unlabeled datasets. ∙ Define partitional clustering as a multiobjective optimization problem. The aim is to obtain well-separated, connected, and compact clusters and for this purpose, two objective functions have been defined based on the concepts of data connectivity and cohesion. These functions are the core of an efficient multiobjective particle swarm optimization algorithm, which has been devised for and applied to automatic grouping of large unlabeled datasets. For that purpose, this thesis is divided is five main parts: ∙ The first part, including Chapter 1, aims at introducing state of the art of swarm intelligence based clustering methods. ∙ The second part, including Chapter 2, consists in clustering analysis with combination of artificial bee colony algorithm and K-means technique. ∙ The third part, including Chapter 3, consists in a presentation of clustering analysis using opposition-based API algorithm. ∙ The fourth part, including Chapter 4, consists in multiobjective clustering analysis using particle swarm optimization. ∙ Finally, the fifth part, including Chapter 5, concludes the thesis and addresses the future directions and the open issues of this research.
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Riyaz, Firasath Maurer Peter M. Marks Robert J. "Evolving a Disjunctive Predator Prey Swarm using PSO Adapting Swarms with Swarms/." Waco, Tex. : Baylor University, 2005. http://hdl.handle.net/2104/1465.

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Frantz, Natalie R. "Swarm intelligence for autonomous UAV control." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Jun%5FFrantz.pdf.

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Berg, Jannik, and Camilla Haukenes Karud. "Swarm intelligence in bio-inspired robotics." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-13684.

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In this report, we have explored swarm intelligence through a box-pushing taskwith physical robots called e-pucks. Research on social insects has been presentedtogether with dierent ways of controlling autonomous robots, where combiningthis knowledge has been essential in our quest to make a biological plausible antretrieving system.Inspired by ants and behavior-based robotics, we have created the system CRABS.It is based on Brooks' subsumption architecture to control six dierent behaviors,from a xed input-output scheme. The system is designed to easily handle addingor removal of behavior layers. Behavior modules can also be used separately andported to other software or hardware platforms.During this project we came across several hardware and software challenges in-vestigating cooperative behavior. With the use of the simulation tool Webots, wewere able to determine e-pucks' capabilities, and through this knowledge able todesign and construct an articial food source. This operated as the box-item in thebox-pushing task.Based on two types of sensors and two actuators (wheels), we had a strategy toaccomplish the box-pushing task following the biological principles of social insects.The guidelines of the ant retrieving model made CRABS a self-organized systemthat given three or more e-pucks, will always succeed in retrieving the box back tothe wall. The most remarkable view on this accomplishment is that is done throughthe use of only stigmergy and positive/negative feedback.One of the things we've experienced throughout this thesis is that hardware is a morework demanding and inconsistent platform than your usual software simulation.Everything is not given, and although Webots provided helpful shortcuts, a lot oftime and hard work was put down in order to get the system up and running. Withthat being said, we are pleased that we took the hardware rout and were able totest and validate our system on physical robots.
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Books on the topic "Swarm Intelligence"

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Blum, Christian, and Daniel Merkle, eds. Swarm Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-74089-6.

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Dorigo, Marco, Mauro Birattari, Gianni A. Di Caro, René Doursat, Andries P. Engelbrecht, Dario Floreano, Luca Maria Gambardella, et al., eds. Swarm Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15461-4.

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Dorigo, Marco, Mauro Birattari, Xiaodong Li, Manuel López-Ibáñez, Kazuhiro Ohkura, Carlo Pinciroli, and Thomas Stützle, eds. Swarm Intelligence. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44427-7.

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Dorigo, Marco, Mauro Birattari, Simon Garnier, Heiko Hamann, Marco Montes de Oca, Christine Solnon, and Thomas Stützle, eds. Swarm Intelligence. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09952-1.

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Dorigo, Marco, Mauro Birattari, Christian Blum, Anders Lyhne Christensen, Andries P. Engelbrecht, Roderich Groß, and Thomas Stützle, eds. Swarm Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32650-9.

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Dorigo, Marco, Thomas Stützle, Maria J. Blesa, Christian Blum, Heiko Hamann, Mary Katherine Heinrich, and Volker Strobel, eds. Swarm Intelligence. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60376-2.

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Dorigo, Marco, Mauro Birattari, Christian Blum, Anders L. Christensen, Andreagiovanni Reina, and Vito Trianni, eds. Swarm Intelligence. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00533-7.

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Dorigo, Marco, Heiko Hamann, Manuel López-Ibáñez, José García-Nieto, Andries Engelbrecht, Carlo Pinciroli, Volker Strobel, and Christian Camacho-Villalón, eds. Swarm Intelligence. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20176-9.

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Slowik, Adam, ed. Swarm Intelligence Algorithms. First edition. | Boca Raton : Taylor and Francis, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/9780429422607.

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Slowik, Adam, ed. Swarm Intelligence Algorithms. First edition. | Boca Raton : Taylor and Francis, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/9780429422614.

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Book chapters on the topic "Swarm Intelligence"

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Sharma, Abhishek, Abhinav Sharma, Jitendra Kumar Pandey, and Mangey Ram. "Swarm Intelligence." In Swarm Intelligence, 1–10. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003090038-1.

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Du, Ke-Lin, and M. N. S. Swamy. "Swarm Intelligence." In Search and Optimization by Metaheuristics, 237–63. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41192-7_15.

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Merkle, Daniel, and Martin Middendorf. "Swarm Intelligence." In Search Methodologies, 213–42. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-6940-7_8.

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Corne, David W., Alan Reynolds, and Eric Bonabeau. "Swarm Intelligence." In Handbook of Natural Computing, 1599–622. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-540-92910-9_48.

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Li, Xiaodong, and Maurice Clerc. "Swarm Intelligence." In Handbook of Metaheuristics, 353–84. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91086-4_11.

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Grosan, Crina, and Ajith Abraham. "Swarm Intelligence." In Intelligent Systems Reference Library, 409–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21004-4_16.

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Martin, Eric, Samuel Kaski, Fei Zheng, Geoffrey I. Webb, Xiaojin Zhu, Ion Muslea, Kai Ming Ting, et al. "Swarm Intelligence." In Encyclopedia of Machine Learning, 946. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_805.

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Beni, Gerardo. "Swarm Intelligence." In Encyclopedia of Complexity and Systems Science, 1–32. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-3-642-27737-5_530-4.

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Beni, Gerardo. "Swarm Intelligence." In Encyclopedia of Complexity and Systems Science, 1–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-642-27737-5_530-5.

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Sarkar, Kanchan, and Sankar Prasad Bhattacharyya. "Swarm Intelligence." In Soft-Computing in Physical and Chemical Sciences, 109–34. Boca Raton : CRC Press, Taylor & Francis, 2018.: CRC Press, 2017. http://dx.doi.org/10.4324/9781315152899-5.

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Conference papers on the topic "Swarm Intelligence"

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Hiura, Takuya, and Shin Morishita. "Application of Swarm Intelligence to a Vibration Monitoring System." In ASME 2017 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/smasis2017-3734.

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The technology of swarm intelligence has been applied to a mechanical vibration monitoring system composed of a network of units equipped with sensors and actuators. The expression of “swarm intelligence” was first used in 1988 in the context of cellular robotic systems, where lots of simple agents may generate self-organized patterns through mutual interactions. There are various examples of the swarm intelligence in the natural environment, a swarm of ants, birds or fish. In this sense, the network of agents in a swarm may have some kind of intelligence or higher function than those appeared in a simple agent, which is defined as the swarm intelligence. The concept of swarm intelligence may be applied in diverse engineering fields such as flexible pattern recognition, adaptive control system, or intelligent monitoring system, because some kind of intelligence may emerge on the network without any special control system. In this study, a simulation model of a five degree-of-freedom lumped mass-spring system was prepared as an example of a mechanical dynamic system. Five units composed of a displacement sensor and a variable damper as actuator were assumed to be placed on each mass of the system. Each unit was connected to each other to exchange the information of state variables measured by sensors on each unit. Because the network of units configured as a mutual connected neural network, a kind of artificial intelligence, the network of units may memorize the several expected vibration-controlled patterns and may produce the signal to the actuators on the unit to reduce the vibration of target system. The simulation results showed that the excited vibration was reduced autonomously by selecting the position where the damping should be applied.
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Eberhart, Russell, Daniel Palmer, and Marc Kirschenbaum. "Beyond computational intelligence: blended intelligence." In 2015 Swarm/Human Blended Intelligence Workshop (SHBI). IEEE, 2015. http://dx.doi.org/10.1109/shbi.2015.7321679.

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Lomuscio, Alessio, and Edoardo Pirovano. "Verifying Fault-Tolerance in Probabilistic Swarm Systems." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/46.

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We present a method for reasoning about fault-tolerance in unbounded robotic swarms. We introduce a novel semantics that accounts for the probabilistic nature of both the swarm and possible malfunctions, as well as the unbounded nature of swarm systems. We define and interpret a variant of probabilistic linear-time temporal logic on the resulting executions, including those arising from faulty behaviour by some of the agents in the swarm. We specify the decision problem of parameterised fault-tolerance, which concerns determining whether a probabilistic specification holds under possibly faulty behaviour. We outline a verification procedure that we implement and use to study a foraging protocol from swarm robotics, and report the experimental results obtained.
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"IEEE Swarm Intelligence Symposium (SIS2007)." In 2007 IEEE Swarm Intelligence Symposium. IEEE, 2007. http://dx.doi.org/10.1109/sis.2007.368018.

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Schumann, Andrew. "From Swarm Simulations to Swarm Intelligence." In 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS). ACM, 2016. http://dx.doi.org/10.4108/eai.3-12-2015.2262484.

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Schumann, Hans, Louis Rosenberg, and Gregg Willcox. "'"Human Swarms” of novice sports fans beat professional handicappers when forecasting NFL football games." In 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003287.

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The biological phenomenon of Swarm Intelligence (SI) enables social species to converge on group decisions by interacting in real-time systems. Studied in schools of fish, bee swarms, and bird flocks, biologists have shown for decades that SI can greatly amplify group intelligence in natural systems. Artificial Swarm Intelligence (ASI) is a computer-mediated technique developed in 2015 to enable networked human groups to form real-time systems that can deliberate and converge on decisions, predictions, estimations, and prioritizations. A unique combination of real-time HCI methods and AI algorithms, ASI technology (also called “Human Swarming” or “Swarm AI”) has been shown in many studies to amplify group intelligence in forecasting tasks, often enabling small groups of non-professionals to exceed expert level performance. In the current study, small groups of approximately 24 amateur sports fans used an online platform called Swarm to collaboratively make weekly predictions (against the spread) of every football game in four consecutive NFL seasons (2019 - 2022) for a total of 1027 forecasted games. Approximately 5 games per week (as forecast by the human swarm) were identified as “predictable” using statistical heuristics. Performance was compared against the Vegas betting markets and measured against accepted performance benchmarks for professional handicappers. It is well known that professional bettors rarely achieve more than 55% accuracy against the Vegas spread and that top experts in the world rarely exceed 58% accuracy. In this study the amateur sports fans achieved 62.5% accuracy against the spread when connected as real-time “swarms.” A statistical analysis of this result (across 4 NFL seasons) found that swarms outperformed the 55% accuracy benchmark for human experts with significance (p=0.002). These results confirmed for the first time that groups of amateurs, when connected in real-time using ASI, can consistently generate forecasts that exceeded expert level performance with a high degree of statistical certainty.Keywords: Swarm Intelligence, Artificial Swarm Intelligence, Collective Intelligence, Wisdom of Crowds, Hyperswarms,
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Dorigo, M. "SWARM-BOT: an experiment in swarm robotics." In 2005 IEEE Swarm Intelligence Symposium. IEEE, 2005. http://dx.doi.org/10.1109/sis.2005.1501622.

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Harwell, John, and Maria Gini. "Swarm Engineering Through Quantitative Measurement of Swarm Robotic Principles in a 10,000 Robot Swarm." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/48.

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When designing swarm-robotic systems, system- atic comparison of algorithms from different do- mains is necessary to determine which is capa- ble of scaling up to handle the target problem size and target operating conditions. We propose a set of quantitative metrics for scalability, flexibility, and emergence which are capable of addressing these needs during the system design process. We demonstrate the applicability of our proposed met- rics as a design tool by solving a large object gath- ering problem in temporally varying operating con- ditions using iterative hypothesis evaluation. We provide experimental results obtained in simulation for swarms of over 10,000 robots.
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"Glowworm swarm optimization, Optimization, Swarm intelligence, Clustering." In 2019 Scientific Conference on Network, Power Systems and Computing. Institute of Electronics and Computer, 2019. http://dx.doi.org/10.33969/eecs.v3.012.

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Eberhart, Russell. "Beyond algorithms: Evolving intelligence." In 2016 Swarm/Human Blended Intelligence Workshop (SHBI). IEEE, 2016. http://dx.doi.org/10.1109/shbi.2016.7780280.

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Reports on the topic "Swarm Intelligence"

1

Fleischer, Mark. Foundations of Swarm Intelligence: From Principles to Practice. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada440801.

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