Relevant bibliographies by topics / Swarm intelligence – Analysis

Contents

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

Academic literature on the topic 'Swarm intelligence – Analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Swarm intelligence – Analysis"

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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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Apostolidis, Georgios K., and Leontios J. Hadjileontiadis. "Swarm decomposition: A novel signal analysis using swarm intelligence." Signal Processing 132 (March 2017): 40–50. http://dx.doi.org/10.1016/j.sigpro.2016.09.004.

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Mashingaidze, Sivave. "Benefits of collective intelligence: Swarm intelligent foraging, an ethnographic research." Journal of Governance and Regulation 3, no. 4 (2014): 193–201. http://dx.doi.org/10.22495/jgr_v3_i4_c2_p2.

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Wisdom of crowds; bees, colonies of ants, schools of fish, flocks of birds, and fireflies flashing synchronously are all examples of highly coordinated behaviors that emerge from collective, decentralized intelligence. This article is an ethnographic study of swarm intelligence foraging of swarms and the benefits derived from collective decision making. The author used using secondary data analysis to look at the benefits of swarm intelligence in decision making to achieve intended goals. Concepts like combined decision making and consensus were discussed and four principles of swarm intelligence were also discussed viz; coordination, cooperation, deliberation and collaboration. The research found out that collective decision making in swarms is the touchstone of achieving their goals. The research further recommended corporate to adopt collective intelligence for business sustainability
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CHEN, Lin, Yanming FAN, Chen WEI, and Haibin DUAN. "Swarm entropy: a quantitative analysis tool for swarm intelligence behaviors." SCIENTIA SINICA Informationis 50, no. 3 (March 5, 2019): 335–46. http://dx.doi.org/10.1360/ssi-2019-0191.

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Bavafa, E., M. J. Yazdanpanah, and B. Kalaghchi. "CHEMOTHERAPY USING LINEAR ANALYSIS AND SWARM INTELLIGENCE." IFAC Proceedings Volumes 41, no. 2 (2008): 5233–38. http://dx.doi.org/10.3182/20080706-5-kr-1001.00879.

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Jr., Iztok Fister, Xin She Yang, Janez Brest, Dušan Fister, and Iztok Fister. "Analysis of randomisation methods in swarm intelligence." International Journal of Bio-Inspired Computation 7, no. 1 (2015): 36. http://dx.doi.org/10.1504/ijbic.2015.067989.

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Yang, Xin-She. "Swarm intelligence based algorithms: a critical analysis." Evolutionary Intelligence 7, no. 1 (December 17, 2013): 17–28. http://dx.doi.org/10.1007/s12065-013-0102-2.

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Deda, Jakub, and Tomasz Mirosław. "Remotely Controlled Robot Swarms: A Structural Analysis and Model for Structural Optimization." Applied Sciences 11, no. 18 (September 14, 2021): 8539. http://dx.doi.org/10.3390/app11188539.

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In this paper, the authors analyse the structure of robot swarms. Drive, communication, and artificial intelligence technologies have reached a level where the inspiration of the animal world has become very useful for the development of systems of which people have dreamed for centuries. A short introduction describes the need for and expectations of autonomous robots and vehicles. A classification of swarm structures is based on animals such as bees or ants. Two main types of swarms are recognized: structural (master–slaves) and non-structural. The operator controls both of them remotely. The swarm structure has a great influence on the structure of single robots. A computer model with an object programming definition was worked out, and a simulation of the presented swarm structure is provided. The results are described in the paper. The algorithm codes analysed in this paper are included in an appendix.
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Yang, Xin-She. "Efficiency Analysis of Swarm Intelligence and Randomization Techniques." Journal of Computational and Theoretical Nanoscience 9, no. 2 (February 1, 2012): 189–98. http://dx.doi.org/10.1166/jctn.2012.2012.

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Chandran, C. P., and E. Kiruba Nesa Malar. "Clustering analysis on molecular docking with swarm intelligence." International Journal of Advanced Intelligence Paradigms 1, no. 1 (2019): 1. http://dx.doi.org/10.1504/ijaip.2019.10024519.

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Dissertations / Theses on the topic "Swarm intelligence – Analysis"

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Wilke, Daniel N. "Analysis of the particle swarm optimization algorithm." Pretoria : [s.n.], 2005. http://upetd.up.ac.za/thesis/available/etd-01312006-125743.

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Gazi, Veysel. "Stability Analysis of Swarms." The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1029812963.

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Brambilla, Manuele. "Formal methods for the design and analysis of robot swarms." Doctoral thesis, Universite Libre de Bruxelles, 2014. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209277.

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In my doctoral dissertation, I tackled two of the main open problems in swarm robotics: design and verification. I did so by using model checking.

Designing and developing individual-level behaviors to obtain a desired swarm-level goal is, in general, very difficult, as it is difficult to predict and thus design the non-linear interactions of tens or hundreds individual robots that result in the desired collective behavior. In my dissertation, I presented my novel contribution to the top-down design of robot swarms: property-driven design. Property-driven design is based on prescriptive modeling and model checking. Using property-driven design it is possible to design robot swarms in a systematic way, realizing systems that are "correct by design". I demonstrated property-driven design on two case-studies: aggregation and foraging.

Developing techniques to analyze and verify a robot swarm is also a necessary step in order to employ swarm robotics in real-world applications. In my dissertation, I explored the use of model checking to analyze and verify the properties of robot swarms. Model checking allows us to formally describe a set of desired properties of a system, in a more powerful and precise way compared to other mathematical approaches, and verify whether a given model of a system satisfies them. I explored two different approaches: the first based on Bio-PEPA and the second based on KLAIM.
Doctorat en Sciences de l'ingénieur
info:eu-repo/semantics/nonPublished

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Harrison, Kyle Robert. "An Analysis of Parameter Control Mechanisms for the Particle Swarm Optimization Algorithm." Thesis, University of Pretoria, 2018. http://hdl.handle.net/2263/66103.

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The particle swarm optimization (PSO) algorithm is a stochastic, population-based optimization technique influenced by social dynamics. It has been shown that the performance of the PSO algorithm can be greatly improved if the control parameters are appropriately tuned. However, the tuning of control parameter values has traditionally been a time-consuming, empirical process followed by statistical analysis. Furthermore, ideal values for the control parameters may be time-dependent; parameter values that lead to good performance in an exploratory phase may not be ideal for an exploitative phase. Self-adaptive algorithms eliminate the need to tune parameters in advance, while also providing real-time behaviour adaptation based on the current problem. This thesis first provides an in-depth review of existing self-adaptive particle swarm optimization (SAPSO) techniques. Their ability to attain order-2 stability is examined and it is shown that a majority of the existing SAPSO algorithms are guaranteed to exhibit either premature convergence or rapid divergence. A further investigation focusing on inertia weight control strategies demonstrates that none of the examined techniques outperform a static value. This thesis then investigates the performance of a wide variety of PSO parameter configurations, thereby discovering regions in parameter space that lead to good performance. This investigation provides strong empirical evidence that the best values to employ for the PSO control parameters change over time. Finally, this thesis proposes novel PSO variants inspired by results of the aforementioned studies.
Thesis (PhD)--University of Pretoria, 2018.
Computer Science
PhD
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Ramanatha, Renu. "A parallel computing test bed for performing an unsupervised fluoroscopic analysis of knee joint kinematics." [Boise, Idaho] : Boise State University, 2009. http://scholarworks.boisestate.edu/td/71/.

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Hosking, Matthew R. "Testability of a swarm robot using a system of systems approach and discrete event simulation /." Online version of thesis, 2009. http://hdl.handle.net/1850/11215.

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Ushie, Ogri James. "Intelligent optimisation of analogue circuits using particle swarm optimisation, genetic programming and genetic folding." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13643.

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This research presents various intelligent optimisation methods which are: genetic algorithm (GA), particle swarm optimisation (PSO), artificial bee colony algorithm (ABCA), firefly algorithm (FA) and bacterial foraging optimisation (BFO). It attempts to minimise analogue electronic filter and amplifier circuits, taking a cascode amplifier design as a case study, and utilising the above-mentioned intelligent optimisation algorithms with the aim of determining the best among them to be used. Small signal analysis (SSA) conversion of the cascode circuit is performed while mesh analysis is applied to transform the circuit to matrices form. Computer programmes are developed in Matlab using the above mentioned intelligent optimisation algorithms to minimise the cascode amplifier circuit. The objective function is based on input resistance, output resistance, power consumption, gain, upperfrequency band and lower frequency band. The cascode circuit result presented, applied the above-mentioned existing intelligent optimisation algorithms to optimise the same circuit and compared the techniques with the one using Nelder-Mead and the original circuit simulated in PSpice. Four circuit element types (resistors, capacitors, transistors and operational amplifier (op-amp)) are targeted using the optimisation techniques and subsequently compared to the initial circuit. The PSO based optimised result has proven to be best followed by that of GA optimised technique regarding power consumption reduction and frequency response. This work modifies symbolic circuit analysis in Matlab (MSCAM) tool which utilises Netlist from PSpice or from simulation to generate matrices. These matrices are used for optimisation or to compute circuit parameters. The tool is modified to handle both active and passive elements such as inductors, resistors, capacitors, transistors and op-amps. The transistors are transformed into SSA and op-amp use the SSA that is easy to implement in programming. Results are presented to illustrate the potential of the algorithm. Results are compared to PSpice simulation and the approach handled larger matrices dimensions compared to that of existing symbolic circuit analysis in Matlab tool (SCAM). The SCAM formed matrices by adding additional rows and columns due to how the algorithm was developed which takes more computer resources and limit its performance. Next to this, this work attempts to reduce component count in high-pass, low-pass, and all- pass active filters. Also, it uses a lower order filter to realise same results as higher order filter regarding frequency response curve. The optimisers applied are GA, PSO (the best two methods among them) and Nelder-Mead (the worst method) are used subsequently for the filters optimisation. The filters are converted into their SSA while nodal analysis is applied to transform the circuit to matrices form. High-pass, low-pass, and all- pass active filters results are presented to demonstrate the effectiveness of the technique. Results presented have shown that with a computer code, a lower order op-amp filter can be applied to realise the same results as that of a higher order one. Furthermore, PSO can realise the best results regarding frequency response for the three results, followed by GA whereas Nelder- Mead has the worst results. Furthermore, this research introduced genetic folding (GF), MSCAM, and automatically simulated Netlist into existing genetic programming (GP), which is a new contribution in this work, which enhances the development of independent Matlab toolbox for the evolution of passive and active filter circuits. The active filter circuit evolution especially when operational amplifier is involved as a component is of it first kind in circuit evolution. In the work, only one software package is used instead of combining PSpice and Matlab in electronic circuit simulation. This saves the elapsed time for moving the simulation between the two platforms and reduces the cost of subscription. The evolving circuit from GP using Matlab simulation is automatically transformed into a symbolic Netlist also by Matlab simulation. The Netlist is fed into MSCAM; where MSCAM uses it to generate matrices for the simulation. The matrices enhance frequency response analysis of low-pass, high-pass, band-pass, band-stop of active and passive filter circuits. After the circuit evolution using the developed GP, PSO is then applied to optimise some of the circuits. The algorithm is tested with twelve different circuits (five examples of the active filter, four examples of passive filter circuits and three examples of transistor amplifier circuits) and the results presented have shown that the algorithm is efficient regarding design.
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Khan, Salman A. "Design and analysis of evolutionary and swarm intelligence techniques for topology design of distributed local area networks." Pretori: [S.n.], 2009. http://upetd.up.ac.za/thesis/available/etd-09272009-153908/.

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Diwold, Konrad. "Natural optimization: An analysis of self-organization principles found in social insects and their application for optimization." Doctoral thesis, Universitätsbibliothek Leipzig, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-86174.

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Das Forschungsfeld Schwarmintelligenz, also die Anwendung des Verhaltens dezentraler selbstorganisierender Tierkollektive, im Kontext der Informatik hat eine Reihe von state-of-the-art Kontroll- und Optimierungsmechanismen hervorgebracht. Die Untersuchung selbstorganisierender biologischer Systeme fördert zum einen das Design neuer robuster und adaptiver Algorithmen. Zum anderen kann sie das Verständnis der Funktionalität von selbstorganisierenden Prinzipien, welche in der Natur auftreten, unterstützen. Diese Arbeit deckt beide zuvor beschriebenen Aspekte ab. Unter Verwendung von Modellen und Simulation werden offene Fragen bezüglich der Organisation und des Verhaltens von sozialen Insekten beleuchtet. Weiter werden Abstraktionen von selbstorganisierenden Konzepten, welche man bei sozialen Insekten findet, genutzt, um neue Methoden zur Optimierung zu entwickeln. Der erste Teil dieser Arbeit untersucht allgemeine Aspekte der Arbeitsteilung sozialer Insekten. Zuerst wird die Anpassungsfähigkeit von unterschiedlich großen Kolonien, bezüglich dynamischer Veränderungen in der Umwelt untersucht. Die Ergebnisse zeigen, dass die Fähigkeit einer Kolonie, auf Veränderung in der Umwelt zu reagieren, von der Koloniegröße beeinflusst wird. Ein weiterer Aspekt der Arbeitsteilung, welcher in dieser Arbeit untersucht wird, ist, inwieweit eine räumliche Verteilung von Aufgaben und Individuen einen Einfluss auf die Arbeitsteilung hat. Die Ergebnisse deuten an, dass soziale Insekten von einer räumlichen Trennung, der zu bewerkstelligenden Aufgaben profitieren, da eine solche Trennung die Produktivität der Kolonie erhöht. Das könnte erklären, warum eine räumliche getrennte Anordnung von Aufgaben und Individuen häufig in realen Kolonien sozialer Insekten beobachtet werden kann. Der zweite Teil dieser Arbeit untersucht verschiedene Aspekte von Selbstorganisation bei Honigbienen. Zunächst wird der Einfluss der räumlichen Verteilung von Nestplätzen auf die Nestplatzsuche der europäischen Honigbiene Apis mellifera untersucht. Die Ergebnisse legen nahe, dass die Nestplatzsuche eines Schwarms aktiv durch die Anordnung der Nestplätze in der Umwelt beeinflusst wird. Eine nestplatzreiche Umgebung kann den Prozess eines Schwarms, sich für einen Nestplatz zu entscheiden, stark behindern. Das könnte erklären, warum Honigbienenarten, die geringe Anforderungen an Nestplätze haben, was die Anzahl von potenziellen Nestplätzen natürlich erhöht, eine sehr ungenaue Form der Nestplatzsuche aufweisen. Ein zweiter Aspekt der Honigbienen, welcher untersucht wird, sind die Steuerungsmechanismen, die dem kollektiven Flug eines Bienenschwarms unterliegen. Zwei mögliche Führungsmechanismen, aktive und passive Führung, werden hinsichtlich ihrer Fähigkeit verglichen, die Flugeigenschaften eines echten Honigbienenschwarms zu reproduzieren. Die Simulationsergebnisse bestätigen aktuelle empirische Befunde und zeigen, dass aktive Führung in der Lage ist, Charakteristika fliegender Schwärme widerzuspiegeln. Bei passiver Führung ist das nicht der Fall. Eine Anwendung biologischer Konzepte im Bereich der Informatik wird anhand der Nestplatzsuche demonstriert. Diese ist ein natürlicher Optimierungsprozess, basierend auf einfachen Regeln. Erzielt wird eine lokale Optimierung, die es einem Schwarm ermöglicht, Nestplätze in einer bisher unbekannten Umgebung zu finden und aus diesen den besten Nestplatz zu wählen. Das ist die Motivation, Nestplatzsuche im Bereich der Optimierung anzuwenden. Hierfür wird zuerst das Optimierungspotenzial der biologischen Nestplatzsuche mit Hilfe eines biologischen Modells untersucht. Basierend auf der Nestplatzsuche wird ein abstrahiertes algorithmisches Schema, das so genannte „Bee Nest-Site Selection Scheme“ (BNSSS) entworfen. Basierend auf dem Schema wird der erste Nestplatzsuche inspirierte Optimierungsalgorithmus „Bee-Nest\\\'\\\' für die Anwendung im Bereich von molekular Docking entwickelt. Im Vergleich zu anderen Optimierungsalgorithmen erzielt „Bee-Nest“ eine sehr gute Leistung
The application in computer science of the behaviour found in decentralized self-organizing animal collectives -- also known as swarm intelligence -- has brought forward a number of state-of-the art control and optimization mechanisms. Further study of such self-organizing biological systems can foster the design of new robust and adaptive algorithms, as well as aid in the understanding of self-organizing processes found in nature. This thesis covers both of the aspects described above, namely the use of computational models to investigate open questions regarding the organization and behaviour of social insects, as well as using the abstraction of concepts found in social insects to generate new optimization methods. In the first part of this work, general aspects of division of labour in social insects are investigated. First the adaptiveness of different-sized colonies to dynamic changes in the environment is analysed. The findings show that a colony\\\'s ability to react to changes in the environment scales with its size. Another aspect of division of labour which is investigated is the extent to which different spatial distributions of tasks and individuals influence division of labour. The results suggest that social insects can benefit from a spatial separation of tasks within their environment, as this increases the colony\\\'s productivity. This could explain why a spatial organization of tasks and individuals is often observed in real social insect colonies. The second part of this work investigates several aspects of self-organization found in honeybees. First the influence of spatial nest-site distribution on the ability of the European honeybee Apis mellifera to select a new nest-site is studied. The results suggest that a swarm\\\'s habitat can influence its decision-making process. Nest-site rich habitats can obstruct a swarm\\\'s ability to choose a single site if all sites are of equal quality. This could explain why in nature honeybee species which have less requirements regarding a new nest-site have evolved a more imprecise form of nest-site selection than cavity-nesting species. Another aspect of honeybees which is investigated is the guidance behaviour in migrating swarms. Two potential guidance mechanisms, active and passive guidance, are compared regarding their ability to reproduce real honeybee swarm flight characteristics. The simulation results confirm previous empirical findings, as they show that active guidance is able to reflect a number of characteristics which can be observed in real moving honeybee swarms, while this is not the case for passive guidance. Nest-site selection in honeybees can be regarded as a natural optimization process. It is based on simple rules and achieves local optimization as it enables a swarm to decide between several potential nest-sites in a previously unknown dynamic environment. These factors motivate the application of the nest-site selection process to the problem domain of function optimization. First, the optimization potential of the biological nest-site selection process is studied. Then a general algorithmic scheme called ``Bee Nest-Site Selection Scheme\\\'\\\' (BNSSS) is introduced. Based on the scheme the first nest-site inspired optimization algorithm ``Bee-Nest\\\'\\\' is introduced and successfully applied to the domain of molecular docking
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Jackson, John Taylor. "Improving Swarm Performance by Applying Machine Learning to a New Dynamic Survey." DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1857.

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A company, Unanimous AI, has created a software platform that allows individuals to come together as a group or a human swarm to make decisions. These human swarms amplify the decision-making capabilities of both the individuals and the group. One way Unanimous AI increases the swarm’s collective decision-making capabilities is by limiting the swarm to more informed individuals on the given topic. The previous way Unanimous AI selected users to enter the swarm was improved upon by a new methodology that is detailed in this study. This new methodology implements a new type of survey that collects data that is more indicative of a user’s knowledge on the subject than the previous survey. This study also identifies better metrics for predicting each user’s performance when predicting Major League Baseball game outcomes throughout a given week. This study demonstrates that the new machine learning models and data extraction schemes are approximately 12% more accurate than the currently implemented methods at predicting user performance. Finally, this study shows how predicting a user’s performance based purely on their inputs can increase the average performance of a group by limiting the group to the top predicted performers. This study shows that by limiting the group to the top predicted performers across five different weeks of MLB predictions, the average group performance was increased up to 5.5%, making this a superior method.
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Books on the topic "Swarm intelligence – Analysis"

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Mauro, Birattari, Blum Christian, Christensen Anders Lyhne, Engelbrecht Andries P, Gross Roderich, Stützle Thomas, and SpringerLink (Online service), eds. Swarm Intelligence: 8th International Conference, ANTS 2012, Brussels, Belgium, September 12-14, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Thrun, Michael Christoph. Projection-Based Clustering through Self-Organization and Swarm Intelligence: Combining Cluster Analysis with the Visualization of High-Dimensional Data. Cham: Springer Nature, 2018.

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Yuhui, Shi, Ji Zhen, and SpringerLink (Online service), eds. Advances in Swarm Intelligence: Third International Conference, ICSI 2012, Shenzhen, China, June 17-20, 2012 Proceedings, Part II. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Yuhui, Shi, Ji Zhen, and SpringerLink (Online service), eds. Advances in Swarm Intelligence: Third International Conference, ICSI 2012, Shenzhen, China, June 17-20, 2012 Proceedings, Part I. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Tan, Ying. Advances in Swarm Intelligence: 4th International Conference, ICSI 2013, Harbin, China, June 12-15, 2013, Proceedings, Part I. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Tan, Ying. Advances in Swarm Intelligence: 4th International Conference, ICSI 2013, Harbin, China, June 12-15, 2013, Proceedings, Part II. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Swagatam, Das, Suganthan Ponnuthurai Nagaratnam, Nanda Pradipta Kumar, and SpringerLink (Online service), eds. Swarm, Evolutionary, and Memetic Computing: Third International Conference, SEMCCO 2012, Bhubaneswar, India, December 20-22, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Nagaratnam, Suganthan Ponnuthurai, Das Swagatam, Satapathy Suresh Chandra, and SpringerLink (Online service), eds. Swarm, Evolutionary, and Memetic Computing: Second International Conference, SEMCCO 2011, Visakhapatnam, Andhra Pradesh, India, December 19-21, 2011, Proceedings, Part II. Berlin, Heidelberg: Springer-Verlag GmbH Berlin Heidelberg, 2011.

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Marcin, Korytkowski, Scherer Rafał, Tadeusiewicz Ryszard, Zadeh Lotfi A, Zurada Jacek M, and SpringerLink (Online service), eds. Swarm and Evolutionary Computation: International Symposia, SIDE 2012 and EC 2012, Held in Conjunction with ICAISC 2012, Zakopane, Poland, April 29-May 3, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Swarm Evolutionary and Memetic Computing Lecture Notes in Computer Science. Springer, 2012.

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Book chapters on the topic "Swarm intelligence – Analysis"

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Shen, Yuanxia, Linna Wei, and Chuanhua Zeng. "Swarm Diversity Analysis of Particle Swarm Optimization." In Advances in Swarm and Computational Intelligence, 99–106. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20466-6_11.

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Röhler, Antonio Bolufé, and Stephen Chen. "An Analysis of Sub-swarms in Multi-swarm Systems." In AI 2011: Advances in Artificial Intelligence, 271–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25832-9_28.

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Ait Adda, Samia, and Amar Balla. "The Use of Ontology in Semantic Analysis of the Published Learners Messages for Adaptability." In Swarm Intelligence Based Optimization, 106–14. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12970-9_12.

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Mishra, B. S. P., Satchidanand Dehuri, and Sung-Bae Cho. "Swarm Intelligence in Multiple and Many Objectives Optimization: A Survey and Topical Study on EEG Signal Analysis." In Multi-objective Swarm Intelligence, 27–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46309-3_2.

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Davidović, Tatjana, and Tatjana Jakšić Krüger. "Convergence Analysis of Swarm Intelligence Metaheuristic Methods." In Communications in Computer and Information Science, 251–66. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93800-4_20.

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Liu, Jinxing, Huanbin Liu, and Wenhao Shen. "Stability Analysis of Particle Swarm Optimization." In Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, 781–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74205-0_82.

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Hamadicharef, Brahim. "Bibliometric Analysis of Particle Swarm Optimization (PSO) Research 2000-2010." In Artificial Intelligence and Computational Intelligence, 404–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23896-3_50.

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Yang, Xin-She, and Xingshi He. "Swarm Intelligence and Evolutionary Computation: Overview and Analysis." In Studies in Computational Intelligence, 1–23. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13826-8_1.

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Shen, Zhe-Ping, and Walter W. Chen. "Directional Analysis of Slope Stability Using a Real Example." In Advances in Swarm and Computational Intelligence, 176–82. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20466-6_19.

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Huang, Lu, and Hong Wang. "Application of Swarm Intelligence Optimization in EEG Analysis." In Lecture Notes in Electrical Engineering, 683–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38524-7_75.

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Conference papers on the topic "Swarm intelligence – Analysis"

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Jiang, Ming, Yupin Luo, and Shiyuan Yang. "Stagnation Analysis in Particle Swarm Optimization." In 2007 IEEE Swarm Intelligence Symposium. IEEE, 2007. http://dx.doi.org/10.1109/sis.2007.368031.

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Pan, Feng, Xiaohui Hu, Russ Eberhart, and Yaobin Chen. "An analysis of Bare Bones Particle Swarm." In 2008 IEEE Swarm Intelligence Symposium (SIS). IEEE, 2008. http://dx.doi.org/10.1109/sis.2008.4668301.

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Bjerknes, Jan Dyre, Alan FT Winfield, and Chris Melhuish. "An Analysis of Emergent Taxis in a Wireless Connected Swarm of Mobile Robots." In 2007 IEEE Swarm Intelligence Symposium. IEEE, 2007. http://dx.doi.org/10.1109/sis.2007.368025.

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Veeramachaneni, Kalyan, Lisa Osadciw, and Ganapathi Kamath. "Probabilistically Driven Particle Swarms for Optimization of Multi Valued Discrete Problems : Design and Analysis." In 2007 IEEE Swarm Intelligence Symposium. IEEE, 2007. http://dx.doi.org/10.1109/sis.2007.368038.

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Zhou, Yongquan, and Bai Liu. "Two Novel Swarm Intelligence Clustering Analysis Methods." In 2009 Fifth International Conference on Natural Computation. IEEE, 2009. http://dx.doi.org/10.1109/icnc.2009.251.

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Fan, Jiaqi, Mengqi Hu, Xianghua Chu, and Dong Yang. "A comparison analysis of swarm intelligence algorithms for robot swarm learning." In 2017 Winter Simulation Conference (WSC). IEEE, 2017. http://dx.doi.org/10.1109/wsc.2017.8248025.

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Cheng, Shi, Yuhui Shi, Quande Qin, and Shujing Gao. "Solution clustering analysis in brain storm optimization algorithm." In 2013 IEEE Symposium on Swarm Intelligence (SIS). IEEE, 2013. http://dx.doi.org/10.1109/sis.2013.6615167.

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Matthysen, W., AP Engelbrecht, and KM Malan. "Analysis of stagnation behavior of vector evaluated particle swarm optimization." In 2013 IEEE Symposium on Swarm Intelligence (SIS). IEEE, 2013. http://dx.doi.org/10.1109/sis.2013.6615173.

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Scheepers, Christiaan, and Andries P. Engelbrecht. "Analysis of stagnation behaviour of competitive coevolutionary trained neuro-controllers." In 2014 IEEE Symposium On Swarm Intelligence (SIS). IEEE, 2014. http://dx.doi.org/10.1109/sis.2014.7011795.

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Wang, Yong, and Jun Chen. "Using ant swarm intelligence for data clustering analysis." In 2009 2nd IEEE International Conference on Computer Science and Information Technology. IEEE, 2009. http://dx.doi.org/10.1109/iccsit.2009.5234535.

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