TÜRKÇE
ENGLISH
Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| ELECTRICAL-ELECTRONICS ENGINEERING (MASTER) (WITH THESIS) (ENGLISH) | |
| Code | EE523 |
| Name | Optimizasyon |
| Term | 2026-2027 Academic Year |
| Term | Fall |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Doç. Dr. FATİH KILIÇ |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
This course aims to introduce the historical development and fundamental approaches of metaheuristic algorithms. The course examines major metaheuristic methods including local search methods, genetic algorithms, evolutionary strategies, evolutionary programming, genetic programming, Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Harris Hawks Optimization (HHO). Furthermore, the course aims to provide an overview of constrained optimization problems, multi-objective optimization, dynamic environments, parallel implementations, co-evolutionary systems, parameter control, hybrid approaches, as well as real-world and commercial applications of these methods.
Course Content
This course covers the historical development and fundamental approaches of metaheuristic algorithms. The main metaheuristic methods such as local search algorithms, genetic algorithms, evolutionary strategies, evolutionary programming, Particle Swarm Optimization, Artificial Bee Colony (ABC) algorithm, and Harris Hawks Optimization are examined. Furthermore, general information is provided on constrained optimization problems, multi-objective optimization, dynamic environments, parallel implementations, collaborative evolutionary systems, parameter control, hybrid approaches, and real-world and commercial applications of these methods.
Course Precondition
There is no prerequisite for the course.
Resources
David B Fogel, "The Handbook of Evolutionary Computation", 1997 IOP Publishing Ltd and Oxford University Press Zbigniew Michalewics, "Genetic Algorithms + Data Structures = Evolution Programs", Springer Verlag, 1997. Melanie Mithcell, "An Introduction to Genetic Algorithms (Complex Adaptive Systems)", MIT Press, 1998 Ed. Bäck, Fogel and Michalewicz, "Evolutionary Computation1: Basic Algorithms and Operators", 2000.
Notes
Yang, X. S. (2010). Engineering optimization: an introduction with metaheuristic applications. John Wiley & Sons. Du, K. L., & Swamy, M. N. S. (2016). Search and optimization by metaheuristics. Techniques and Algorithms Inspired by Nature, 1-10.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explains the fundamental terminology related to metaheuristic search algorithms and metaheuristic optimization. |
| LO02 | Explains the essential requirements that metaheuristic search algorithms must satisfy. |
| LO03 | Explains the lifecycle of metaheuristic search algorithms. |
| LO04 | Identifies the core components of metaheuristic optimization. |
| LO05 | Explains the experimental testing and validation processes in metaheuristic optimization studies. |
| LO06 | Solves continuous optimization problems in engineering using metaheuristic algorithms. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Being able to specialize in at least one of the branches that form the foundations of electrical-electronic engineering by increasing the level of knowledge beyond the undergraduate level. | 4 |
| PLO02 | Bilgi - Kuramsal, Olgusal | To comprehend the integrity of all the subjects included in the field of specialization. | 3 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Knowing and following the current scientific literature in the field of specialization | 3 |
| PLO04 | Bilgi - Kuramsal, Olgusal | To be able to comprehend the interdisciplinary interaction of the field with other related branches. | 2 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Ability to do theoretical and experimental work | 4 |
| PLO06 | Bilgi - Kuramsal, Olgusal | To create a complete scientific text by compiling the information obtained from the research. | |
| PLO07 | Bilgi - Kuramsal, Olgusal | To work on the thesis topic programmatically, following the logical integrity required by the subject within the framework determined by the advisor. | |
| PLO08 | Bilgi - Kuramsal, Olgusal | To search for literature in scientific databases, particularly the ability to correctly and accurately scan databases and evaluate and categorize listed items. | 2 |
| PLO09 | Bilgi - Kuramsal, Olgusal | Knowledge of English at a level that can easily read and understand a scientific text written in English in the field of specialization | |
| PLO10 | Bilgi - Kuramsal, Olgusal | Compile information on his/her expertise in a presentation format and present it understandably and effectively. | 2 |
| PLO11 | Bilgi - Kuramsal, Olgusal | Ability to write a computer program in a familiar programming language, generally for a specific purpose, specifically related to the field of expertise. | |
| PLO12 | Bilgi - Kuramsal, Olgusal | Being able to guide and take the initiative in environments that require solving problems related to the field | 4 |
| PLO13 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Ability to communicate with people in an appropriate language | |
| PLO14 | Yetkinlikler - Öğrenme Yetkinliği | To be able to produce projects, policies, and processes in the field of expertise and to evaluate these elements | |
| PLO15 | Yetkinlikler - Öğrenme Yetkinliği | Ability to research new topics based on existing research experience | 3 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Optimization, Optimization Terminology and Definitions | Study basic optimization concepts and terminology. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 2 | Engineering Optimization, Optimization Type, Optimization Algorithms, Research and Application Project | Read about types of optimization problems (continuous, discrete). | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 3 | Creating Cost Function and CEC Functions | Review objective functions and benchmark (CEC) functions. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 4 | Meta-Heuristic Algorithm Test Problems, Measurement and comparison of search performances of meta-heuristic | (exploration) ve sömürü (exploitation) kavramlarını öğrenin. Learn exploration vs exploitation and local search concepts. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 5 | Meta Heuristic Algorithms: Hill climbing, Tabu search | Study principles of hill climbing and tabu search. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 6 | Meta Heuristic Algorithms: Genetic Algorithm and Its Application, Research and Application Project Control 1 | Learn GA components (selection, crossover, mutation). | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 7 | Meta Heuristic Algorithms: Genetic Algorithm and Its Application, Research and Application Project Control 2 | Study GA parameter tuning and applications. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 8 | Mid-Term Exam | Review all topics from first 7 weeks | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Meta Heuristic Algorithms: Particle Swarm Optimization, Research and Application Project Control | Study swarm intelligence and PSO basics. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 10 | Meta Heuristic Algorithms: Artificial Bee Colony Algorithm and Its Application | Learn principles of ABC algorithm. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 11 | Meta Sezgisel Algoritmalar: Grey Wolf Optimizer | Study hunting and leadership mechanism in GWO | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 12 | Transfer functions for Binary Search Space | Review binary encoding and transfer functions. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 13 | Hybrid algorithms | Study hybridization of optimization algorithms. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 14 | Student Presentations | Prepare project results and practice presentation. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 15 | Review | Review and compare all algorithms. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 16 | Term Exams | Review all course materials. | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Complete missing topics and revise. | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 16 | 5 | 80 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 24 | 24 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 2 | 2 |
| Final Exam | 1 | 2 | 2 |
| Total Workload (Hour) | 150 | ||
| Total Workload / 25 (h) | 6,00 | ||
| ECTS | 6 ECTS | ||