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040676 KU Metaheuristics (MA) (2018W)

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

Registration/Deregistration

Details

max. 30 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Wednesday 03.10. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 10.10. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 17.10. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 24.10. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 31.10. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 07.11. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 14.11. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 21.11. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 28.11. 11:30 - 13:00 Seminarraum 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
Wednesday 28.11. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 05.12. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 12.12. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 09.01. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 16.01. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 23.01. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 30.01. 13:15 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß

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 understand 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

Five short written tests during the course, no material allowed: 50% (5x10%)
Project work (choose one): 40%
- programming a metaheuristic for an optimisation problem
- read and study a scientific paper
Oral presentation of the project: 10%

Minimum requirements and assessment criteria

At the end of this course, students will know what metaheuristics are, why they are needed, how to design them, and how to evaluate their quality.

Examination topics

Reading list

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 07.09.2020 15:29