Swarm Intelligence

Description

This course provides a deep knowledge about swarm intelligence in technical systems. In swarm intelligence, we deal with a group of simple and usually homogenous individuals with simple rules. The swarm can achieve a complex and intelligent behavior using local interactions between its members. This collective property can be used in technical systems as well as in optimization of complex problems. One advanced application of swarm intelligence is in the area of 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, analyzed 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 

Part 2: Swarm and multi-agent systems

  • Division of labor and task allocation
  • Swarm clustering and sorting
  • Ant systems and optimization

Part 3: Applications

  • Swarm localization and display
  • Swarm robotics

Lecturer

Lectures 

The lectures take place: Wednesday 11:00 -12:30 in G29 – 307. The first Lecture will take palce on Wednesday 10.10.2018 (Please note, we start at 11:00)

 

Slides

 

Recorded lectures

The recordings are now avaiable (update 15.10.2018).

Please refer to http://mediasite.ovgu.de/Mediasite/Catalog/catalogs/iks-si for the recorded lectures. Note: Here you require your URZ account. 

 

Tutorials:

In order to be able to write the exam you must fulfill the following criteria: 

  1. Attend the tutorials.
  2. The assignments need to be solved at home, before the tutorial.
  3. The solutions of the assignments will be presented by the students in the tutorials.
  4. In order to get the permission for writing the exam:
    1. Each student must present the solution for at least two assignments on two different tutorial days.
    2. In addition, the students must deliver a list of at least one assignment on 4 tutorial days. With this list, the students inform the tutor about the assignments they are willing to present on that tutorial day, at least half of all Assignments need to be solved. The tutor will select the presenters from the list.

 

             Tutorial 1      Tutorial 2      Tutorial 3      Tutorial 4   Tutorial 5
Group 1  23.10., 09:15 am  13.11., 09:15 am  27. 11., 09.15 am  11. 12., 09:15 am  08. 01., 09:15 am
Group 2  23.10., 11:15 am  13.11., 11:15 am  27. 11., 11:15 am   11. 12., 11:15 am  08. 01., 11:15 am
Group 3  06.11., 09:15 am  20.11., 09:15 am  04.12., 09:15 am  18. 12., 09:15 am  15. 01., 09:15 am
Group 4  06.11., 11:15 am  20.11., 11:15 am  04.12., 11:15 am  18. 12., 11:15 am   15. 01., 11:15 am
Group 5  25.10., 03:15 pm  15.11., 03:15 pm  29.11., 03.15 pm  13. 12., 03:15 pm  tba

 

Assignments

  • New assignments will be published after the lecture
  • Assignments for Chapter 2
    • For Tutorial 1 solve assignments 1 to 7
    • For the programming task download Swarm-Follow.ipynb
  • Assignments for Chapter 3
    • For Tutorial 2 solve the remaining assignments for chapter 2 and assignments 1 to 3 of chapter 3.
    • Group 1 and 2 only need to solve the remaining assignments for chapter 2
    • For Tutorial 3
      • Group 1 and 2 solve the assigments 1-8 for chapter 3
      • Group 3, 4, 5 solve the assignments 4-10
    • For Tutorial 4
      • Group 1 and 2 solve the assignments 9-15 for chapter 3
      • Group 3, 4, 5 solve the remaining assignments for chapter 3 and additionally assignment 1 and 2 from chapter 4.
  • Assignments for Chapter 4

 

 Literature

  • Veysel Gazi and Kevin M. Passino, Swarm Stability and Optimization, Springer, 2011
  • Eric Bonabeau, Marco Dorigo and Guy Theraulaz, Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, 1999
  • Andries 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
  • Marco Dorigo and Thomas Stützle, Ant Colony Optimization, The MIT Press, 2004
  • C. Solnon: Ant Colony Optimization and Constraint Programming. 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

Last Modification: 12.12.2018 - Contact Person:

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