Information Package / Course Catalogue
| Course Code | : MCE505 |
| Course Type | : Area Elective |
| Couse Group | : Second Cycle (Master's Degree) |
| Education Language | : English |
| Work Placement | : N/A |
| Theory | : 3 |
| Prt. | : 0 |
| Credit | : 3 |
| Lab | : 0 |
| ECTS | : 6 |
The objective of this course is to provide students with a solid understanding of optimization techniques and algorithms, enabling them to analyze, design, and implement effective optimization solutions for engineering applications. Students will develop critical thinking and problem-solving skills necessary to optimize engineering systems efficiently.
This course covers the fundamentals of optimization, including mathematical foundations and Monte Carlo methods, various optimization algorithms such as Genetic Algorithms, Simulated Annealing, Ant Algorithms, Bee Algorithms, Particle Swarm Optimization, Harmony Search, and Firefly Algorithm. Additionally, the course includes multi-objective optimization techniques.
| 1. | Students will be able to demonstrate a solid understanding of optimization using metaheuristic techniques. |
| 2. | Students will be proficient in various metaheuristic optimization algorithms and be able to code these algorithm using programming languages. |
| 3. | Students will gain the ability to metaheuristic algorithms to solve real-world engineering problems effectively. |
| 4. | Students will develop problem-solving abilities, allowing them to analyze, design, and implement efficient optimization solutions in various engineering applications. |
| 5. | Students will be able to implement the models required by their field of expertise using programming languages, and to program algorithms to evaluate the obtained outputs using numerical and graphical methods. |
| 1. | Engineering Optimization: An Introduction with Metaheuristic Applications (Yang, 2011) |
| 2. | Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks (Pham, Karaboğa, 2000) |
| Type of Assessment | Count | Percent |
|---|---|---|
| Presentation | 1 | %20 |
| Assignment | 1 | %20 |
| Quiz | 2 | %10 |
| Final Examination | 1 | %50 |
| Activities | Count | Preparation | Time | Total Work Load (hours) |
|---|---|---|---|---|
| Lecture - Theory | 14 | 1 | 3 | 56 |
| Assignment | 1 | 0 | 24 | 24 |
| Presentation | 1 | 15 | 0 | 15 |
| Reading | 2 | 0 | 10 | 20 |
| Quiz | 2 | 3 | 0 | 7 |
| Final Examination | 1 | 25 | 3 | 28 |
| TOTAL WORKLOAD (hours) | 150 | |||
PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | PÇ-6 | PÇ-7 | PÇ-8 | PÇ-9 | PÇ-10 | PÇ-11 | PÇ-12 | PÇ-13 | |
OÇ-1 | 3 | 4 | 4 | 5 | 3 | 4 | 4 | 4 | 5 | 3 | 3 | 3 | 5 |
OÇ-2 | 3 | 4 | 4 | 4 | 5 | 4 | 3 | 5 | 5 | 3 | 4 | 3 | 5 |
OÇ-3 | 5 | 5 | 5 | 5 | 4 | 5 | 4 | 5 | 5 | 4 | 5 | 5 | 3 |
OÇ-4 | 5 | 5 | 5 | 5 | 5 | 5 | 4 | 5 | 5 | 5 | 4 | 5 | 5 |
OÇ-5 | 4 | 5 | 5 | 5 | 5 | 5 | 4 | 5 | 5 | 5 | 3 | 4 | 4 |