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040676 KU Metaheuristics (MA) (2020W)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work


Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first serve).


max. 30 participants
Language: English


Classes (iCal) - next class is marked with N

Wednesday 07.10. 11:30 - 13:00 Digital
Wednesday 14.10. 11:30 - 13:00 Digital
Wednesday 21.10. 11:30 - 13:00 Digital
Wednesday 28.10. 11:30 - 13:00 Digital
Wednesday 04.11. 11:30 - 13:00 Digital
Wednesday 11.11. 11:30 - 13:00 Digital
Wednesday 18.11. 11:30 - 13:00 Digital
Wednesday 25.11. 11:30 - 13:00 Digital
Wednesday 02.12. 11:30 - 13:00 Digital
Wednesday 09.12. 11:30 - 13:00 Digital
Wednesday 16.12. 11:30 - 13:00 Digital
Wednesday 13.01. 11:30 - 13:00 Digital
Wednesday 20.01. 11:30 - 13:00 Digital
Wednesday 27.01. 11:30 - 13:00 Digital


Aims, contents and method of the course

Metaheuristics are general high-level procedures that coordinate simple heuristics and rules to find high-quality solutions to difficult optimization problems. They are based on distinct paradigms and offer different mechanisms to go beyond the first solution obtained that cannot be improved by local search. They are frequently built upon a number of common building blocks such as greedy algorithms, randomization, neighborhoods and local search, reduced neighborhoods and candidate lists, intensification, diversification, path-relinking, and periodical restarts. Metaheuristics are among the most effective solution strategies for solving combinatorial optimization problems in practice and very frequently produce much better solutions than those obtained by the simple heuristics and rules they coordinate.
Metaheuristics are particularly attractive in the efficient and effective solution of logistic decision problems in supply chains, transportation, telecommunications, vehicle routing and scheduling, manufacturing and machine scheduling, timetabling, sports scheduling, facility location and layout, and network design, among other areas.

The objective of this course is to provide students with the fundamental tools for designing, tuning, and testing heuristics and metaheuristics for hard combinatorial optimization problems. Besides that, we will also cover the fundamental concepts of complexity theory that are the key to understanding the need for approximate approaches and to design efficient heuristics and metaheuristics.
1. A gentle introduction to the analysis of algorithms and complexity theory
2. Historical and modern local search methods
3. Nature-inspired metaheuristics
4. Construction-based metaheuristics

Assessment and permitted materials

* [40%] Four short exams á ca. 15 minutes (written, 10% each)
* [45%] Project work (choose one):
- Programming a metaheuristic for an optimisation problem
- Read and study (i.e., summarise, analyse and criticise) a scientific paper
* [15%] Oral presentation of project

Minimum requirements and assessment criteria

In order to obtain a positive grade on the course, at least 50% of the overall points have to be achieved. The grades are distributed as follows:
1: 87% to 100%
2: 75% to <87%
3: 63% to <75%
4: 50% to <63%
5: <50%

Examination topics

* Analysis of algorithms and complexity theory (basics)
* Local search methods
* Nature-inspired metaheuristics
* Construction-based metaheuristics

Reading list

The teaching material (slides, sample code, further reading, etc.) is available on the e-learning platform Moodle.

Useful literature:
1. M. Gendreau and J.-Y. Potvin (2010), editors, Handbook of Metaheuristics, 2nd edition, Springer, 648 pages.
2. E. K. Burke and G. Kendall (2014), editors, Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, 2nd edition, Springer, 716 pages.
3. H. H. Hoos and T. Stützle (2005), Stochastic Local Search: Foundations and Applications, Elsevier, 658 pages.

Association in the course directory

Last modified: Mo 05.10.2020 10:08