Relevant bibliographies by topics / Swerm intelligensie

Contents

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

Academic literature on the topic 'Swerm intelligensie'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Swerm intelligensie"

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Aziz, Nor Azlina Ab, Marizan Mubin, Zuwairie Ibrahim, and Sophan Wahyudi Nawawi. "Statistical Analysis for Swarm Intelligence — Simplified." International Journal of Future Computer and Communication 4, no. 3 (2015): 193–97. http://dx.doi.org/10.7763/ijfcc.2015.v4.383.

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Takanobu, Hideaki, Masumi Iida, Kenji Suzuki, Hirofumi Miura, Masanao Futakami, Tomohiro Endo, and Yoshinobu Inada. "Swarm Intelligence Robot : 3D swarm motion by airship and mobile robots." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2010.5 (2010): 61–66. http://dx.doi.org/10.1299/jsmeicam.2010.5.61.

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Sharma, Pallavi, and Rajesh Kochher. "Enhanced RZ-Leach using Swarm Intelligence Technique." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (February 28, 2018): 693–700. http://dx.doi.org/10.31142/ijtsrd8315.

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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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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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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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Eichmann, Christian, and Carsten Mueller. "Team Formation Based on Nature-Inspired Swarm Intelligence." Journal of Software 10, no. 3 (March 2015): 344–54. http://dx.doi.org/10.17706/jsw.10.3.344-354.

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Shi, Yuhui. "Developmental Swarm Intelligence." International Journal of Swarm Intelligence Research 5, no. 1 (January 2014): 36–54. http://dx.doi.org/10.4018/ijsir.2014010102.

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In this paper, the necessity of having developmental learning embedded in a swarm intelligence algorithm is confirmed by briefly considering brain evolution, brain development, brainstorming process, etc. Several swarm intelligence algorithms are looked at from developmental learning perspective. Finally, a framework of a developmental swarm intelligence algorithm is given to help understand developmental swarm intelligence algorithms, and to guide to design and/or implement any new developmental swarm intelligence algorithm and/or any developmental evolutionary algorithm.
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Chittka, L., and A. Mesoudi. "Insect Swarm Intelligence." Science 331, no. 6016 (January 27, 2011): 401–2. http://dx.doi.org/10.1126/science.1199780.

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Dissertations / Theses on the topic "Swerm intelligensie"

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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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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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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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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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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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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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Hula, Tomáš. "Experimenty s rojovou inteligencí (swarm intelligence)." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2008. http://www.nusl.cz/ntk/nusl-235936.

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This work deals with the issue of swarm intelligence as a subdiscipline of artificial intelligence. It describes biological background of the dilemma briefly and presents the principles of searching paths in ant colonies as well. There is also adduced combinatorial optimization and two selected tasks are defined in detail: Travelling Salesman Problem and Quadratic Assignment Problem. The main part of this work consists of description of swarm intelligence methods for solving mentioned problems and evaluation of experiments that were made on these methods. There were tested Ant System, Ant Colony System, Hybrid Ant System and Max-Min Ant System algorithm. Within the work there were also designed and tested my own method Genetic Ant System which enriches the basic Ant System i.a. with development of unit parameters based on genetical principles. The results of described methods were compared together with the ones of classical artificial intelligence within the frame of both solved problems.
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Garattoni, Lorenzo. "Cognitive Abilities in Swarm Robotics: Developing a swarm that can collectively sequence tasks." Doctoral thesis, Universite Libre de Bruxelles, 2021. https://dipot.ulb.ac.be/dspace/bitstream/2013/317235/5/contratLG.pdf.

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Can robots of a swarm cooperate to solve together a complex cognitive problem that none of them can solve alone? TS-Swarm is a robot swarm that autonomously sequences tasks at run time and can therefore operate even if the correct order of execution is unknown at design time. The ability to sequence tasks endows robot swarms with unprecedented autonomy and is an important step towards the uptake of swarm robotics in a range of practical applications.
Doctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished
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Montes, De Oca Roldan Marco. "Incremental social learning in swarm intelligence systems." Doctoral thesis, Universite Libre de Bruxelles, 2011. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209909.

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A swarm intelligence system is a type of multiagent system with the following distinctive characteristics: (i) it is composed of a large number of agents, (ii) the agents that comprise the system are simple with respect to the complexity of the task the system is required to perform, (iii) its control relies on principles of decentralization and self-organization, and (iv) its constituent agents interact locally with one another and with their environment.

Interactions among agents, either direct or indirect through the environment in which they act, are fundamental for swarm intelligence to exist; however, there is a class of interactions, referred to as "interference", that actually blocks or hinders the agents' goal-seeking behavior. For example, competition for space may reduce the mobility of robots in a swarm robotics system, or misleading information may spread through the system in a particle swarm optimization algorithm. One of the most visible effects of interference in a swarm intelligence system is the reduction of its efficiency. In other words, interference increases the time required by the system to reach a desired state. Thus, interference is a fundamental problem which negatively affects the viability of the swarm intelligence approach for solving important, practical problems.

We propose a framework called "incremental social learning" (ISL) as a solution to the aforementioned problem. It consists of two elements: (i) a growing population of agents, and (ii) a social learning mechanism. Initially, a system under the control of ISL consists of a small population of agents. These agents interact with one another and with their environment for some time before new agents are added to the system according to a predefined schedule. When a new agent is about to be added, it learns socially from a subset of the agents that have been part of the system for some time, and that, as a consequence, may have gathered useful information. The implementation of the social learning mechanism is application-dependent, but the goal is to transfer knowledge from a set of experienced agents that are already in the environment to the newly added agent. The process continues until one of the following criteria is met: (i) the maximum number of agents is reached, (ii) the assigned task is finished, or (iii) the system performs as desired. Starting with a small number of agents reduces interference because it reduces the number of interactions within the system, and thus, fast progress toward the desired state may be achieved. By learning socially, newly added agents acquire knowledge about their environment without incurring the costs of acquiring that knowledge individually. As a result, ISL can make a swarm intelligence system reach a desired state more rapidly.

We have successfully applied ISL to two very different swarm intelligence systems. We applied ISL to particle swarm optimization algorithms. The results of this study demonstrate that ISL substantially improves the performance of these kinds of algorithms. In fact, two of the resulting algorithms are competitive with state-of-the-art algorithms in the field. The second system to which we applied ISL exploits a collective decision-making mechanism based on an opinion formation model. This mechanism is also one of the original contributions presented in this dissertation. A swarm robotics system under the control of the proposed mechanism allows robots to choose from a set of two actions the action that is fastest to execute. In this case, when only a small proportion of the swarm is able to concurrently execute the alternative actions, ISL substantially improves the system's performance.
Doctorat en Sciences de l'ingénieur
info:eu-repo/semantics/nonPublished

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Books on the topic "Swerm intelligensie"

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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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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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Computational swarm intelligence. Hoboken, NJ: Wiley, 2005.

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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 "Swerm intelligensie"

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Beni, Gerardo. "Swarm Intelligence." In Computational Complexity, 3150–69. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1800-9_195.

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Beni, Gerardo. "Swarm Intelligence." In Encyclopedia of Complexity and Systems Science, 8869–88. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-30440-3_530.

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Shukla, Anupam, Ritu Tiwari, and Rahul Kala. "Swarm Intelligence." In Towards Hybrid and Adaptive Computing, 187–207. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14344-1_9.

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Beni, Gerardo. "Swarm Intelligence." In Complex Social and Behavioral Systems, 791–818. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0368-0_530.

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Cui, Xiaohui. "Intelligence, Swarm." In Encyclopedia of Sciences and Religions, 1064–67. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-1-4020-8265-8_1475.

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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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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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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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Conference papers on the topic "Swerm intelligensie"

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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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"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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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. "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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Jakimovski, Bojan, Benjamin Meyer, and Erik Maehle. "Swarm intelligence for self-reconfiguring walking robot." In 2008 IEEE Swarm Intelligence Symposium (SIS). IEEE, 2008. http://dx.doi.org/10.1109/sis.2008.4668286.

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Conforth, Matthew, and Yan Meng. "Reinforcement learning for neural networks using swarm intelligence." In 2008 IEEE Swarm Intelligence Symposium (SIS). IEEE, 2008. http://dx.doi.org/10.1109/sis.2008.4668289.

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Johnson, Cameron, Ganesh K. Venayagamoorthy, and Parviz Palangpour. "Hardware implementations of Swarming Intelligence — a survey." In 2008 IEEE Swarm Intelligence Symposium (SIS). IEEE, 2008. http://dx.doi.org/10.1109/sis.2008.4668331.

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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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Fortier, Nathan, John Sheppard, and Karthik Ganesan Pillai. "Bayesian abductive inference using overlapping swarm intelligence." In 2013 IEEE Symposium on Swarm Intelligence (SIS). IEEE, 2013. http://dx.doi.org/10.1109/sis.2013.6615188.

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Fortier, Nathan, John Sheppard, and Shane Strasser. "Learning Bayesian classifiers using overlapping swarm intelligence." In 2014 IEEE Symposium On Swarm Intelligence (SIS). IEEE, 2014. http://dx.doi.org/10.1109/sis.2014.7011796.

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Reports on the topic "Swerm intelligensie"

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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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