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Seminar Swarm-based Computation with Applications in Bioinformatics


Admission requirements

Bachelor course Natural Computing is a helpful prerequisite, but not mandatory.
No specific other requirements.


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 unsophisticated entities that cooperate in order to exhibit a desired behavior (self-organization). For example, without any master blueprint bees are able to build 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:

  • Ant Colony Optimization

  • Artificial Bee Colonies

  • Particle Swarm Optimization

  • Swarm Robotics

  • Firefly algorithms

  • Division of Labor Models

  • Principles of Self-Organization in Social Insects

  • Agent Based Modeling and its Relation to Swarm Based Computing

  • Differential Evolution, Gravitational Search and Related Metaheuristics

Application areas include specific bioinformatics applications, such that bioinformatics students can specialize on 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 matter

  • 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 consists of

  • The project report (40%)

  • The presentation slides and oral presentation (40%)

  • The demonstration of the implementation/software (20%)

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.


  • You have to sign up for courses and exams (including retakes) in uSis. Check this link for information about how to register for courses.

Contact information

Lecturer: dr. Hao Wang
Website: Swarm-based Computation