Examination arrangement

Course content

This course is designed to present an overview of Swarm Intelligence (SI) topic, including both behavioral swarm Intelligence and computational swarm intelligence, and applications of SI. The students will learn different swarm intelligence algorithms that are inspired by natural systems such as ant colonies, bird flocking, animal herding, bacterial growth, fish schooling and microbial intelligence. The students will implement different swarm intelligence algorithms, visualize and apply them to solve real problems such as optimization problems.

Course topics: There are four main topics:

1. Agent-based modeling: Bottom-up modeling method. individual agents. System theory and complex systems. Multi-agent systems.
2. Behavioral swarm intelligence: Modeling flocking behavior. Boids model. Flocking behavior applications, such as agents queuing and homing.

3. Computational swarm intelligence (CSI): Optimization theory and multi-objective optimization. Particle swarm optimization (PSO) Ant colony optimization (ACO). Bees colony algorithm (BCO). Bats algorithm

4. Selected applications: Different selected application where the students can apply the swarm intelligence algorithms to solve real problems, such as:

• Multi-robot path planning
• Task scheduling.
• Etc.

Learning outcome

Upon completion of the course, students will be expected to:

Knowledge:

• Have knowledge of individual/intelligent agents for modeling of industrial, social and biological systems.
• Have knowledge of modeling swarms/social agents in complex landscapes. 
• Have knowledge of swarm intelligence algorithms inspired by different natural systems.

Skills:

• Have practice in programming virtual worlds in a game engine.
• Have skills in using individual/intelligent agents to solve optimization problems in complex landscapes.
• Have skills in developing simulation models based on swarms of intelligent agents.
• Have skills in using swarm intelligence algorithms to solve real optimization problems.

General competence:

• Have general knowledge about the subject's possibilities and limitations.
• Have general knowledge of being able to analyze, disseminate and communicate the topic issues.
• Have general knowledge about how intelligent agents can contribute to innovation processes.

Learning methods and activities

Lectures, discussion at group and class level, exercises, student presentations, mandatory assignments covering the whole course. The mandatory assignments are performed individually or in groups of 2-3 students.

A number of mandatory assignments must be passed for permission to enter the oral exam. 

Compulsory assignments

• Mandatory assignments

Further on evaluation

Oral exam based on the obligatory assignments and course content.

Specific conditions

Course materials

The course material will be taken from different books and white papers. Main course textbook:

Andries P. Engelbrecht, Fundamentals of computational swarm intelligence, Wiley (2015), ISBN: 978-0-470-09191-3.

Other relevant textbooks:

• Anand Nayyar, Dac-Nhuong Le, Nhu Gia Nguyen, Advances in Swarm Intelligence for Optimizing Problems in Computer Science, 1st Edition, (2018), Chapman and Hall/CRC, ISBN 9781138482517.
• James Kenndey and Rusell C. Eberhart, Swarm Intelligence, Mkf (2001), ISBN: 978-1-55860-595-4.
• Dan Simon. Evolutionary Optimization Algorithms: Biologically Inspired and Population-based Approaches to Computer Intelligence, Wiley, 2013 Ed.
• Jun Sun, Choi-Hong Lai and Xiao-Jun Wu, Particle Swarm Optimization - Classical and Quantum perspectives, CRC Press (2019), ISBN 9780367381936
• Harald Yndestad: Swarm Intelligence Tutotial
• Harald Yndestad: Agents and Landscapes as Complex Systems

Other reading materials and tutorials will be announced at the beginning of the course.