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186.112 Heuristic Optimization Techniques
Diese Lehrveranstaltung ist in allen zugeordneten Curricula Teil der STEOP.
Diese Lehrveranstaltung ist in mindestens einem zugeordneten Curriculum Teil der STEOP.

2019W, VU, 2.0h, 3.0EC

Merkmale

  • Semesterwochenstunden: 2.0
  • ECTS: 3.0
  • Typ: VU Vorlesung mit Übung

Lernergebnisse

Nach positiver Absolvierung der Lehrveranstaltung sind Studierende in der Lage verschiedene heuristische Algorithmen zur Lösung schwieriger Optimierungsprobleme zu verstehen, anzuwenden und für neue Probleme zu adaptieren.

Inhalt der Lehrveranstaltung

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.

In this course we primarily focus on discrete appilcation problems and application in areas such as transport optimization, scheduling, network design, cutting and packing.

The methods considered in the course 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
  • Ant Colony Optimization
  • Hybridization of different approaches, parallelization
  • Analysis and Tuning 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.

Also we will discuss how to properly tune heuristics and to evaluate and compare them by means of experiments and appropriate statistical methods.

Methoden

Introduction and explanation of general methods, discussion of examples, theoretical exercises, hands-on programming exercises, presentation and discussion of solutions.

Prüfungsmodus

Prüfungsimmanent

Weitere Informationen

ECTS-Breakdown

20h Lectures
  5h Recap lecture contents
40h Exercises
  8h Exam preparation
  2h Exercise interviews / Examination
----
75h

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

Vortragende

Institut

LVA Termine

TagZeitDatumOrtBeschreibung
Di.11:00 - 13:0001.10.2019 Zemanek HSVorlesung
Di.11:00 - 13:0008.10.2019 - 21.01.2020Seminarraum FAV 05 (Seminarraum 186) Vorlesung
Heuristic Optimization Techniques - Einzeltermine
TagDatumZeitOrtBeschreibung
Di.01.10.201911:00 - 13:00 Zemanek HSVorlesung
Di.08.10.201911:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Vorlesung
Di.15.10.201911:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Vorlesung
Di.22.10.201911:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Vorlesung
Di.29.10.201911:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Vorlesung
Di.05.11.201911:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Vorlesung
Di.12.11.201911:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Vorlesung
Di.19.11.201911:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Vorlesung
Di.26.11.201911:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Vorlesung
Di.03.12.201911:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Vorlesung
Di.10.12.201911:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Vorlesung
Di.17.12.201911:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Vorlesung
Di.07.01.202011:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Vorlesung
Di.14.01.202011:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Vorlesung
Di.21.01.202011:00 - 13:00Seminarraum FAV 05 (Seminarraum 186) Vorlesung

Leistungsnachweis

Assignments / final oral exam

During the course two assignments have to be solved and handed in. Each assignment consists of a theoretical exercise part and a programming exercise for which concise reports have to be prepared. The assignments 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 solutions of the assignments. The second interview is in connection with an oral examination about the course topics. The assignments 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.

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Curricula

Literatur

  • 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)

Vorkenntnisse

Basic knowledge in algorithms and data structures, programming skills

Vorausgehende Lehrveranstaltungen

Begleitende Lehrveranstaltungen

Vertiefende Lehrveranstaltungen

Weitere Informationen

Sprache

Englisch