186.112 Heuristic Optimization Techniques
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2018W, VU, 2.0h, 3.0EC


  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VU Lecture and Exercise

Aim of course

The aim of this course is to teach the necessary skills to succesfully apply heuristic search methods to problems of theoretical and practical nature.

Subject of course

This lecture deals with heuristic methods to solve optimization problems. The presented approaches are especially suitable for problems arising in practice. On the one hand such problems are often too complex to be solved in an exact way because of the increasing amount of computation time needed by conventional exact techniques. On the other hand it is often sufficient or even required to come up with a good solution in reasonable time.

Areas of application:

  • Combinatorial or logistical problems such as scheduling, timetable creation, cutting and packing, network design, routing
  • Parameter optimization of non-linear or numerical functions
  • Optimization of non-linear problems (e.g. neural networks, rules for classification systems, electronic circuits)
  • Optimization of time dependent or noisy problems

The methods presented include:

  • Construction Heuristics
  • Local Search
  • Simulated Annealing
  • Tabu-Search
  • Guided Local Search
  • Variable Neighborhood Search
  • Very Large Neighborhood Search
  • Greedy Randomized Adaptive Search Procedure
  • Genetic Algorithms
  • Evolutionary Strategies
  • Genetic Programming
  • Ant Colony Optimization
  • Hybridization of different approaches, Parallelization
  • Analysis, Tuning, and Racing of Metaheuristics

Beside the theoretical basics this lecture focuses on practical applications and the connection of metaheuristics with problem-specific heuristics as well as some examples of suitable combinations with exact methods.

Additional information


20h Lectures
  5h Recap lecture contents
40h Programming exercises
  9h Exam preparation
  1h Exercise interviews / Examination

Hotline for any questions concerning this course: heuopt (at) ac.tuwien.ac.at

ACO Simulation: http://web.eecs.utk.edu/~mclennan/Classes/420-594-F04/experiments/ResnickAnts.html



Course dates

Tue11:00 - 13:0002.10.2018 - 22.01.2019Seminarraum FAV 05 (Seminarraum 186) Lecture
Heuristic Optimization Techniques - Single appointments
Tue02.10.201811:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Tue09.10.201811:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Tue16.10.201811:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Tue23.10.201811:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Tue30.10.201811:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Tue06.11.201811:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Tue13.11.201811:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Tue20.11.201811:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Tue27.11.201811:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Tue04.12.201811:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Tue11.12.201811:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Tue18.12.201811:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Tue08.01.201911:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Tue15.01.201911:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Tue22.01.201911:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Lecture

Examination modalities

Programming assignments / final oral exam

During the course two programming exercises and short reports have to be solved and handed in. The exercises are meant to be solved in teams of two students. Each team will present their solution in two interviews.

To complete the course it is mandatory to solve and hand in the programming excercises and short reports. The second interview is in connection with an oral examination about the course topics. The programming excercises and the oral exam each contribute one half to the final grade and each of them has to be positive to successfully complete the lecture.

Course registration

Begin End Deregistration end
01.10.2018 00:00 18.10.2018 23:55 28.10.2018 23:59



  • F. Glover, G. A. Kochenberger: Handbook of Metaheuristics, Kluwer Academic Publishers, 2003
    (comprehensive, recent standard work on metaheuristics)
  • M. Gendreau, J.-Y. Potvin: Handbook of Metaheuristics, 2nd edition, Springer, 2010
    (describes various methods in addition to the first version)
  • E. Talbi: Metaheuristics: From Design to Implementation, J. Wiley and Sons, 2009
    (new and detailed work about metaheuristics)

Preceding courses

Accompanying courses

Continuative courses