Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
HEURISTIC AND METAHEURISTIC OPTIMIZATION METHODS 803400815161 3 + 0 3.0 10.0

Prerequisites None

Language of Instruction English
Course Level Graduate Degree
Course Type Elective
Mode of delivery
Course Coordinator
Instructors Asım Egemen YILMAZ
Assistants
Goals The aim of this course is to introduce the concepts of optimization and constraint satisfaction; conventional and heuristic methods used throughout optimization; introduce the operation principles and details of the aformentioned methods; to apply the relevant methods to the daily life problems.
Course Content Throughout this course, the following topics will be covered: "Conventional Optimization", "Heuristic Optimization". For this purpose, methods such as "Simulated Annealing", "Genetic Algorithm", "Particle Swarm Optimization", "Ant Colony Optimization", "Differential Evolution", "Genetic Programming" and similar algorithms/techniques will be examined in details.
Learning Outcomes 1) Knows the principles, techniques, methods and applications related to data and information analysis.
2) Apply data and information analysis techniques.
3) Can use data and information analysis tools.

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Introduction to Optimization: Historical Development Lecture

 Homework
2. Week Fundamentals of Calculus: Single and Multi Valued Functions, Continuity, Differentiability, Derivative and Gradient Lecture

 Homework
3. Week Classification of Optimization Problems Lecture

 Homework
4. Week Constraint Satisfaction Problems Lecture

 Homework
5. Week Classical Optimization Methods Lecture

 Homework
6. Week Classical Optimization Methods Lecture

 Homework
7. Week Heuristic Optimization Methods: Simulated Annealing Lecture

 Homework
8. Week Heuristic Optimization Methods: Genetic Algorithm Lecture

 Homework
9. Week Heuristic Optimization Methods: Particle Swarm Optimization Lecture

 Homework
10. Week Heuristic Optimization Methods: Ant Colony Optimization Lecture

 Homework
11. Week Heuristic Optimization Methods: Differential Evolution and Other Optimization Algorithms Lecture

 Homework
12. Week Genetik Programlama; Makine Öğrenmesinde Optimizasyonun Yeri ve Önemi Lecture

 Homework
13. Week Multi Objective Optimization Lecture

 Homework
14. Week Project Presentations Lecture

 Homework

Sources Used in This Course
Recommended Sources
A. Antoniou, W.-S. Lu, Practical Optimization: Algorithms and Engineering Applications, Springer Science + Business Media, 2007 (ISBN: 978-0-387-71106-5).
A. Ravindran, K. M. Ragsdell, G. V. Reklaitis, Engineering Optimization: Methods and Applications, Second Edition, John Wiley & Sons, Inc., 2006 (ISBN: 978-0-471-55814-9).
S. S. Rao, Engineering Optimization: Theory and Practice, Third Edition, John Wiley & Sons, Inc., 1996 (ISBN: 0-471-55034-5).

Relations with Education Attainment Program Course Competencies
Program RequirementsContribution LevelDK1DK2DK3
PY15000
PY25000
PY35000

*DK = Course's Contrubution.
0 1 2 3 4 5
Level of contribution None Very Low Low Fair High Very High
.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 3
Work Hour outside Classroom (Preparation, strengthening) 14 9
Homework 8 5
Presentation (Including Preparation Time) 1 20
Midterm Exam 1 1
Time to prepare for Midterm Exam 1 20
Final Exam 1 1
Time to prepare for Final Exam 1 40
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
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Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • Relations with Education Attainment Program Course Competencies
  • ECTS credits and course workload