# 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization This course is in all assigned curricula part of the STEOP.\$(function(){PrimeFaces.cw("Tooltip","widget_j_id_20",{id:"j_id_20",showEffect:"fade",hideEffect:"fade",target:"isAllSteop"});});This course is in at least 1 assigned curriculum part of the STEOP.\$(function(){PrimeFaces.cw("Tooltip","widget_j_id_22",{id:"j_id_22",showEffect:"fade",hideEffect:"fade",target:"isAnySteop"});}); 2021W 2017S

2021W, VU, 2.5h, 4.0EC, to be held in blocked form

## Properties

• Semester hours: 2.5
• Credits: 4.0
• Type: VU Lecture and Exercise
• Format: Presence

## Learning outcomes

After successful completion of the course, students are able to understand diverse heuristic and hybrid algorithms for solving hard combinatorial  optimization problems, to apply them in practice, and to adapt them to new problems.

## Subject of course

Combinatorial optimization problems arise in many aspects of human activities. Examples concern packing and cutting problems, timetabling problems, and vehicle routing problems. Many of these problems are computationally very difficult to be solved to optimality. Therefore, simple heuristics (such as greedy algorithms) and metaheuristics (such as tabu search, evolutionary algorithms, and simulated annealing) have achieved a lot of attention from the optimization community during the last decades. At the same time, the operations research community has invested considerable efforts into both exact techniques (such as algorithms based on branch & bound) and general purpose solvers that implement these state-of-the-art exact techniques. Example are CPLEX and Gurobi. This course will give an introduction to these topics. Attendees should bring a laptop with a recent Linux operating system (e.g. Ubuntu) and the GNU g++ compiler installed.

## Teaching methods

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

Oral

## Additional information

ECTS Breakdown:

* 20 hours of theory classes (0.8 ECTS)
* 10 hours of reading homework (0.4 ECTS)
* 70 hours work on the practical project: 2.8 ECTS. Approximate division of these 70 hours:
+ Getting familiar with the given problem and the required tasks: 5 hours
+ Design and implementation of the heuristics and metaheuristics: 20 hours
+ Design and implementation of the hybrid metaheuristics: 10 hours
+ Experimental evaluation: 15 hours
+ Writing the report: 20 hours

## Course dates

DayTimeDateLocationDescription
Mon11:00 - 13:0017.01.2022EI 1 Petritsch HS 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Tue12:00 - 14:0018.01.2022EI 1 Petritsch HS 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Wed12:00 - 15:0019.01.2022EI 1 Petritsch HS 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Thu10:00 - 13:0020.01.2022EI 3A Hörsaal 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Fri12:00 - 14:0021.01.2022EI 1 Petritsch HS 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Mon11:00 - 13:0024.01.2022EI 1 Petritsch HS 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Tue12:00 - 14:0025.01.2022EI 1 Petritsch HS 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Wed12:00 - 15:0026.01.2022EI 1 Petritsch HS 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Thu10:00 - 13:0027.01.2022EI 3A Hörsaal 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Fri12:00 - 15:0028.01.2022EI 1 Petritsch HS 186.860 Metaheuristics and Hybrid Methods for Combinatorial Optimization
Course is held blocked

## Examination modalities

Programming assignments, discussion of results, and oral exam.

## Course registration

Begin End Deregistration end
22.09.2021 00:00 20.01.2022 23:59

## Literature

No lecture notes are available.

## Previous knowledge

Solid knowledge in programming, algorithms and data structures.

English