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Seminar Swarm-based Computation with Applications in Bioinformatics


Admission requirements

Prior to taking the course, students are suggested to obtain a good understanding of Statistics, Probability Theory, and simple Linear Algebra (all on the bachelor level).


Swarm based computing is a modern artificial intelligence discipline that is concerned with the design of multi-agent systems with applications. It is embedded in the biological study of self-organized behaviors in social animals, e.g., the collective behavior of social insects such as ants and bees, as well as flocks of birds and schools of fish. Instead of a sophisticated controller that governs the global behavior of the system, the swarm intelligence principle is based on many simple entities that cooperate in order to exhibit a self-organizing behavior. For example, without any master blueprint, bees are able to build a complex hive in cooperation.
Scientists have applied these principles to new approaches, for instance, optimization and swarm robotics. Through this research seminar, we will provide an overview of swarm intelligence as well as a collection of some of the most interesting up-to-date applications from different domains. Furthermore, students will learn studying, processing, and presenting scientific material about swarm intelligence through this seminar.
Techniques from swarm-based computing considered in the seminar include the following:

  • Principles of Self-Organization in Social Insects

  • Ant Colony Optimization

  • Artificial Bee Colonies

  • Particle Swarm Optimization

  • Differential Evolution, Gravitational Search and Related Metaheuristics

  • Swarm Robotics

Application areas include specific bioinformatics applications, such that bioinformatics students can specialize in an application in their field of interest. Selected applications include, e.g., sequence alignment and protein folding.

Course objectives

  • Comprehend the basic principle of swarm computing and understand the working mechanism of some algorithms in swarm computing, e.g., Ant Colony Optimization

  • Study and understand scientific literature reflecting state-of-the-art work in swarm computing approaches and related fields

  • Work in groups and conduct group discussions including critical reflections about the subject material

  • Write a scientific essay summarizing the literature read about a particular topic swarm based computing

  • Prepare and give a presentation about a particular topic in swarm-based computing

  • Understand and implement specific swarm-based computing algorithm and, if possible, apply it to a small test problem in the relevant application area chosen.


The most recent timetable can be found at the students' website.

Mode of instruction

  • Writing a scientific essay/project report

  • Oral presentation

  • Group discussion

  • Collaboration with other students

  • Practical work with software (implementations of swarm based algorithms)

Course Load

Hours of Study: 168 (= 6 EC)
Lectures: 10
Literature research: 48
Presentation and preparations: 10
Practical work: 40
Report: 60

Assessment method

The final grade is determined by

  • Assigments (30%)

  • The project essay/report written by each group (40%)

  • The group presentation (30%)

The teacher will inform the students how the inspection of and follow-up discussion of the exams will take place.

Reading list

The following books are recommended reading but not mandatory for the course:

  • Ant Colony Optimization, Marco Dorigo and Thomas Stützle, the MIT press, 2004

  • Swarm Intelligence, James Kennedy, Russell C. Eberhart, and Yuhui Shi, Academic press, 2001

  • Metaheuristics: from design to implementation, Talbi, El-Ghazali, Vol. 74. John Wiley & Sons, 2009

  • Slides will be provided for download.


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Please also register for the course in Brightspace.


Lecturer: dr. Hao Wang