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Information
| Code | SD0581 |
| Name | Optimization Techniques for Engineering Application |
| Term | 2023-2024 Academic Year |
| Duration (T+A) | 2-0 (T-A) (17 Week) |
| ECTS | 3 ECTS |
| National Credit | 2 National Credit |
| Teaching Language | Türkçe |
| Level | Üniversite Dersi |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Öğr. Gör.Dr. Murat ÇIKAN |
Course Goal / Objective
The aim of this course is to provide undergraduate students with an understanding and application of meta-heuristic optimization techniques so that they can effectively solve complex problems in engineering. The course provides students with a comprehensive understanding of various meta-heuristic optimization techniques, while covering in detail the basic principles, operation and application areas of these techniques. The main objectives of the course are: Understanding Basic Concepts: To introduce students to the basic concepts, principles, and applications of meta-heuristic optimization so that they can understand why these techniques are needed and what types of problems they are effective for. Learning Different Techniques: To teach students meta-heuristic optimization methods such as particle swarm optimization, gray wolf search algorithm, multiobjective optimization, etc. in detail. The working principles, advantages and limitations of each method are emphasized to provide students with a broad perspective. Develop Practical Application Skills: To teach students how to apply these techniques to real-world engineering problems. Students will learn how to define problems, determine fitness functions, and apply optimization algorithms in different scenarios to generate solutions to real-world problems. Strengthening Analytical Thinking Skills: To develop students' analytical skills in problem analysis, algorithm selection, interpretation of results and decision making. In this way, students will be able to critically evaluate optimization strategies and improve their applications. Increasing Programming and Software Skills: Provide students with basic MATLAB programming skills and teach them how to code algorithms such as particle swarm optimization. By providing students with innovative and effective approaches to solve complex problems in engineering, this course aims to prepare them to be more competent and successful engineers in their future careers.
Course Content
1- An overview of meta-heuristic optimization techniques and their application areas. 2- Particle Swarm Optimization: Positional memory, velocity update, fitness function, and application examples. 3- Grey Wolf Optimization Algorithm: Updating positions of alpha, beta, gamma wolves and defining fitness functions. 4- Demonstrating methods for multi-objective optimization through Pareto-Front approach and the examination of weight coefficient function method. 5- Testing algorithm performance using statistical approaches. 6- A basic overview of Matlab program and defining variables. 7- Coding Particle Swarm Algorithm in Matlab program. 8-Applying meta-heuristic optimization techniques to real-world problems
Course Precondition
Resources
Lecturer notes
Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Understanding Basic Concepts: Students understand the basic concepts, principles and application areas of meta-heuristic optimization. |
| LO02 | Recognizing Optimization Techniques: Students are introduced to meta-heuristic optimization techniques such as Particle Swarm Optimization and Gray Wolf Search Algorithm and understand how they work. |
| LO03 | Developing Application Skills: Students learn how to apply different optimization techniques to real-world engineering problems. They can define fitness functions, set parameters and implement algorithms. |
| LO04 | Understanding Multiple Optimization and Pareto-Front Approach: Students understand the importance of multi-objective optimization and understand multiple optimization methods using the Pareto-Front approach. |
| LO05 | Performance Evaluation: Students will be able to test the performance of optimization algorithms using statistical approaches and interpret the results. |
| LO06 | MATLAB Programming Skills: Students gain basic skills in the MATLAB programming language and develop the ability to code algorithms such as the Particle Swarm Algorithm. |
| LO07 | Application to Real World Problems: Students can apply the optimization techniques they learn to real-world engineering problems and see the effectiveness of these techniques. |
| LO08 | Analytical Thinking and Evaluation Skills: Students develop analytical thinking skills such as problem analysis, algorithm selection and interpretation of results. They also gain the ability to critically evaluate optimization strategies. |
| LO09 | Project Execution Skills: By selecting and developing their own projects, students gain the ability to apply optimization techniques and present their projects effectively. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | The aim and objectives of the course | Research on the subject | Öğretim Yöntemleri: Soru-Cevap, Tartışma, Anlatım |
| 2 | Basic principles and importance of meta-heuristic optimization. An overview of meta-heuristic optimization techniques and application areas | Research on the subject | Öğretim Yöntemleri: Tartışma, Alıştırma ve Uygulama, Anlatım |
| 3 | Basic principles and operation of the Particle Swarm Optimization algorithm. | Research on the subject | Öğretim Yöntemleri: Benzetim, Proje Temelli Öğrenme , Anlatım |
| 4 | Explanation of the concepts of positional memory and velocity updating in PSO algorithm. Fitness function design and optimization examples | Research on the subject | Öğretim Yöntemleri: Tartışma, Beyin Fırtınası, Alıştırma ve Uygulama, Anlatım |
| 5 | Working logic and basic concepts of Grey Wolf Search Algorithm Location update and movement strategies of alpha, beta, gamma wolves | Research on the subject | Öğretim Yöntemleri: Soru-Cevap, Alıştırma ve Uygulama, Tartışma, Anlatım |
| 6 | Definition of fitness functions in gray wolf algorithm and optimization examples | Research on the subject | Öğretim Yöntemleri: Soru-Cevap, Tartışma, Alıştırma ve Uygulama, Anlatım |
| 7 | The importance and challenges of multi-objective optimization | Research on the subject | Öğretim Yöntemleri: Alıştırma ve Uygulama, Benzetim, Anlatım |
| 8 | Mid-Term Exam | Research on the subject | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | 1-Principles of the Pareto-Front approach 2-Examination of the weight coefficient function method and multiple optimization examples | Research on the subject | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Beyin Fırtınası |
| 10 | Performance Tests of Algorithms-1 Testing the performance of algorithms with statistical approaches | Research on the subject | Öğretim Yöntemleri: Anlatım, Tartışma, Alıştırma ve Uygulama |
| 11 | Performance Tests of Algorithms-2 1-Comparison metrics and analysis 2-Interpretation of test results and evaluation of results | Research on the subject | Öğretim Yöntemleri: Anlatım, Tartışma, Alıştırma ve Uygulama |
| 12 | MATLAB Basics and Particle Swarm Algorithm Coding-1 Introduction to MATLAB | Research on the subject | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 13 | MATLAB Basics and Particle Swarm Algorithm Coding-2 Variable definitions and basic commands Learning how to code the Particle Swarm Algorithm in MATLAB | Research on the subject | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Benzetim |
| 14 | Real World Applications and Student Projects-1 Application of meta-heuristic optimization techniques to real-world problems | Research on the subject | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama |
| 15 | Real World Applications and Student Projects-2 Students select their own projects and apply optimization techniques. Presentation and discussion of student projects | Research on the subject | Öğretim Yöntemleri: Soru-Cevap, Tartışma, Alıştırma ve Uygulama, Anlatım |
| 16 | Term Exams | Research on the subject | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Research on the subject | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 2 | 28 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 1 | 14 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 3 | 10 | 30 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 5 | 5 |
| Final Exam | 1 | 10 | 10 |
| Total Workload (Hour) | 87 | ||
| Total Workload / 25 (h) | 3,48 | ||
| ECTS | 3 ECTS | ||