Universität Wien
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040676 KU Metaheuristics (MA) (2024S)

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

Registration/Deregistration

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

Details

max. 30 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Tuesday 05.03. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 19.03. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 09.04. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 16.04. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 23.04. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 30.04. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 07.05. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 14.05. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 21.05. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 28.05. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 04.06. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 11.06. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 18.06. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 25.06. 11:30 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock

Information

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. The outline of the covered topics will be:
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

For assessment, students will have to do a project work (in groups of up to 3 people), which they also have to present, and there will be an exam.

The course will be structured as follows:
* 8 lectures (mainly presentation by lecturer with some interactive elements, 05.03. - 14.05.2024)
* 1 Q&A-Session (21.05.2024)
* 1 Exam (28.05.2024)
* 2 dates for project work presentations (18.06. & 25.06.2024)
* Deadline for handing in the project work: 28.06.2024

Assessment and permitted materials

* [45%] Exam
- 90 minutes
- pen-and-paper, closed book
* [45%] Project work (choose one):
- Mini-Coding-Project: implement a metaheuristic for an optimization problem
- Literature work: read a scientific article, summarize, analyze, and criticize it
* [10%] 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

* Handbook of Metaheuristics, Michel Gendreau & Jean-Yves Potvin, International Series in Operations Research & Management Science, Springer, ISBN 978-3-319-91085-7
* Handbook of Metaheuristics, Fred Glover & Gary A. Kochenberger, Kluwer’s International Series, ISBN 1-4020-7263-5
* Stochastic Local Search, Foundations and Applications, Holger H. Hoos & Thomas Stützle, Elsevier, ISBN 1-55860-872-9
* Search Methodologies, Introductory Tutorials in Optimization and Decision Support Techniques, Edmund K. Burke & Graham Kendall, Springer, ISBN 0-387-23460-8

Association in the course directory

Last modified: Tu 13.02.2024 14:05