312817 Masterseminar: Metaheuristics (S) (SoSe 2018)

Metaheuristics are widely used in industry and academia to solve large optimization problems that are too difficult to solve using standard modelling techniques like mixed-integer programming. This course will will investigate well-known metaheuristic methods like genetic algorithms, ant colony optimization, variable neighborhood search, large neighborhood search, simulated annealing and tabu search, as well as lesser known approaches such as adaptive decompositions and hybrid approaches. Furthermore, the course will show how to effectively and efficiently model optimization problems within metaheuristic frameworks, paying special attention to details like initial solution construction, neighborhood design, incremental objective evaluation, and overall software system considerations.

The seminar will combine student presentations on metaheuristic topics from the literature with paper presentations and a project (either programming or literature review) in order to provide competence in both the breadth of metaheuristic research, as well as practical knowledge about the implementation of metaheuristics on real problems. The seminar will be conducted completely in English and will be in a block format, with dates to be announced.