Swarm Intelligence
(in German: Swarm Intelligence )
Module-ID: FIN-INF-102419 |
| Link: | LSF |
| Responsibility: | Sanaz Mostaghim |
| Lecturer: | Sanaz Mostaghim |
| Classes: |
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| Applicability in curriculum: | - M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Learning Methods and Models for Data Science - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Computer Science |
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Abbreviation SI |
Credit Points 6 |
Semester Winter |
Term ab 1. |
Duration 1 Semester |
Language english |
Level Master |
Intended learning outcomes:
The students will get an understanding about Swarm Intelligence and its definitions. They will get the ability to develop swarm intelligence algorithms such as swarm aggregation, swarm movements, tracking, tracing and optimization. In addition, they learn the foundations in collective decision-making and their applications. The students can implement, analyse and evaluate swarm algorithms on several applications.
Content:
This course provides in-depth knowledge about swarm intelligence in technical systems. In swarm intelligence, we deal with a group of simple and usually homogenous individuals with simple rules. Usually, swarms can achieve a complex and intelligent behaviour using local interactions between its members. This collective property can be used in technical systems. One advanced application of swarm intelligence is in swarm robotics, in which simple small robots can collectively learn to achieve some predefined complex tasks. During this course, the algorithms of swarm intelligence are presented, analysed and compared. The following topics will be covered:
Part 1: Fundamentals of swarm intelligence
- Swarm stability and stability analysis
- Swarm aggregation
- Swarm in known environments
- Swarm in unknown environments: Particle Swarm Optimization
- Dynamic Optimization
- Multi-Objective Particle Swarm Optimization
- Division of labour and task allocation
- Swarm clustering and sorting
- Ant systems and optimization
- Swarm localization and display
- Swarm robotics
Workload:
Präsenzzeit: 2 SWS Vorlesung 2 SWS Übungen Selbstständige Arbeit: Bearbeiten von Übungs- und Programmieraufgaben 180 h = 56 h Präsenzzeit + 124 h selbstständige Arbeit
| Pre-examination requirements: | Type of examination: | Teaching method / lecture hours per week (SWS): |
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Zum Bestehen der Prüfung oder zum Erwerb eines Scheins sind folgende Leistungen zu erbringen: - Erwerb der Zulassungsvoraussetzungen zur Klausur Inhaltsverzeichnis Teil A (Winter) Seite 229 – Teil A Inhaltsverzeichnis Teil B (Gesamt) - Bestehen der schriftlichen Prüfung, 120 Min. Die Zulassungsvoraussetzungen können aus verschiedenen Elementen bestehen, bspw. dem Lösen und Präsentieren von Übungsaufgaben oder dem Bestehen einer Zwischenklausur im Semester. Die genauen Zulassungsvoraussetzungen werden zum Anfang der Vorlesung, spätestens bis zum Ende der dritten Vorlesungswoche, auf der Webseite des Lehrstuhls bekannt gegeben. |
Vorlesung (2 SWS) Übung (2 SWS) |
| Prerequisites according to examination regulations: | Recommended prerequisites: |
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keine |
Informatik (Algorithmen und Datenstrukturen, Maschinelles Lernen) |
| Media: | Literature: |
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Eric Bonabeau, Marco Dorigo and Guy Theraulaz, Swarm In-telligence: From Natural to Artificial Systems, Oxford University Press, 1999Andries Engelbrecht, Fundamentals of Computational Swarm Intelligence, Wiley 2006 James Kennedy and Russel Eberhart, Swarm Intelligence, Morgan Kaufmann, 2001 Zbigniew Michalewicz and David Fogel, How to solve it: Modern Heuristics, Springer, 2001 Veysel Gazi, Stability Analysis of Swarms, The Ohio State University, 2002 Marco Dorigo and Thomas Stützle, Ant Colony Optimization, The MIT Press, 2004 C. Solnon: Ant Colony Optimization and Constraint Program-ming. Wiley 2010 Gerhard Weiss, Multiagent Systems: A modern approach to distributed artificial systems, The MIT Press, 2000 Christian Müller-Schloer, Hartmut Schmeck and Theo Ungerer, Organic Computing – A Paradigm Shift for Complex Systems, Springer, 2011
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