Description of Individual Course Units
 Course Unit Code Course Unit Title Type of Course Unit Year of Study Semester Number of ECTS Credits İST421 OPTIMIZATION Elective 4 7 5
Level of Course Unit
First Cycle
Objectives of the Course
The aim of the course is to make students understand optimization concept and its background, model non linear optimization problems, comprehend the approaches used to solve this type of problems.
Name of Lecturer(s)
Doç. Dr. Ali MERT-Öğr. Grv. Dr. Aslı KILIÇ
Learning Outcomes
 1 To be able to distinguish non-linear optimization problems from other type of optimization problems. 2 To be able to model a non linear optimization problem. 3 To be able to express a modeled non linear optimization problem graphically. 4 To be able to state fundamentals of non linear optimization correctly. 5 To be able to choose proper method to solve any non linear optimization problem. 6 To be able to state algorithmically a non linear optimization problem solving method. 7 To be able to solve a non linear optimization problem with the best proper method. 8 To be able to interpret the solution of a non linear optimization problem.
Mode of Delivery
Face to Face
Prerequisites and co-requisities
Recommended Optional Programme Components
Course Contents
Fundamentals of non linear optimization. Derivative based and line search based problem solving methods. Problem solving methods for one dimensional and multi-dimensional problems. Heuristic methods for solving optimization problems.
Weekly Detailed Course Contents
 Week Theoretical Practice Laboratory 1 Basic principles, definitions of linear and non linear optimization. Problem solving with guidance 2 Fundamentals of unrestricted non linear optimization problems. Fundamentals of restricted non linear optimization problems. Problem solving with guidance 3 Principles of Detailed search and Dichotomous search and their algorithms. Problem solving with guidance 4 Principles of Golden section and Fibonacci methods and their algorithms. Problem solving with guidance 5 Principles of Bisecting and Newton methods and their algorithms. Problem solving with guidance 6 Principles of Quasi Newton and Secant methods and their algorithms. Problem solving with guidance 7 Principles of Random search and Cyclic Coordinates methods and their algorithms. Problem solving with guidance 8 9 Principles of Hooke - Jeeves and Powell methods and their algorithms. Problem solving with guidance 10 Evolutionary algorithms and applications. Problem solving with guidance 11 Evolutionary algorithms and applications. Problem solving with guidance 12 Nearest Neighborhood algorithm and applications. Problem solving with guidance 13 Artificial Bee Colony algorithm and applications. Problem solving with guidance 14 Ant Colony algorithm and applications. Problem solving with guidance 15 Simulated Annealing algorithm and applications. Problem solving with guidance 16 Final Exam
DERS KİTABI: 1. Yapay Zeka Optimizasyon Algoritmaları, D. Karaboğa, Atlas Yayın dağıtım, 2004. 2. G. ORAL, Doğrusal Olmayan Programlama, Akademi Matbaası, Ankara, 1989. YARDIMCI KİTAPLAR: 3. M.S. BAZARAA and C.M. SHETTY, Nonlinear Programming Theory and Algorithms, John Wiley and Sons, 1989. 4. Yöneylem Araştırması, H. A. TAHA (Çevirenler: Ş. A. BARAY ve Ş. ESNAF), 5. Basım, Literatür Yayıncılık, İstanbul, 2005.
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
 Term (or Year) Learning Activities Quantity Weight SUM 0 End Of Term (or Year) Learning Activities Quantity Weight SUM 0 SUM 0
Language of Instruction
Turkish
Work Placement(s)