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| ELKL5925 | Engineering Optimization | 3+0+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Second Cycle | | Status | Elective | | Department | DEPARTMENT of ELECTRICAL and ELECTRONICS ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Yahya DANAYİYEN | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The aim of this course is to provide fundamental and advanced knowledge of optimization theory and techniques. The course enables students to properly formulate optimization problems, develop mathematical models, and define objective functions and constraint conditions. In addition, students are expected to understand classical optimization methods as well as heuristic and metaheuristic optimization algorithms, apply these techniques to complex problems, analyze optimization results, and develop effective solution strategies to improve system performance. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Explain and classify the fundamental concepts and terminology related to optimization. | 1 | 1,6 | | PO - 2 : | Formulate optimization problems mathematically and construct appropriate mathematical models. | 1 - 2 | 1,6 | | PO - 3 : | Define appropriate objective functions and constraint conditions for optimization problems. | 1 - 3 | 1,6 | | PO - 4 : | Analyze linear and nonlinear optimization problems and compare different solution approaches. | 1 - 3 | 1,6 | | PO - 5 : | Apply heuristic and metaheuristic optimization algorithms to solve complex optimization problems. | 2 - 3 | 1,6 | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| This course is designed for master?s and doctoral students from all engineering disciplines and aims to provide a comprehensive understanding of optimization theory and advanced optimization techniques used in engineering systems. The course covers fundamental optimization terminology and the systematic formulation and mathematical modeling of optimization problems, including the definition of objective and cost functions and the representation of equality and inequality constraints. Classical optimization methods such as unconstrained and constrained optimization and linear programming are examined in depth through analytical derivations and numerical examples. Furthermore, heuristic and metaheuristic optimization algorithms are introduced to address complex, nonlinear, and high-dimensional engineering problems. By the end of the course, students are expected to formulate optimization problems, select appropriate solution strategies, implement optimization algorithms, and critically evaluate optimization results for real-world engineering applications using analytical and simulation-based approaches. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction to Optimization | | | Week 2 | Basic Optimization Terms | | | Week 3 | Formulation of Optimization Problems | | | Week 4 | Mathematical Modeling of Optimization Problems | | | Week 5 | Unconstrained Optimization and Linear Programming | | | Week 6 | Constrained Optimization and Quadratic Programming | | | Week 7 | Constrained Optimization and Quadratic Programming | | | Week 8 | Nonlinear Optimization Problems | | | Week 9 | Mid-term exam | | | Week 10 | Classification of Optimization Methods | | | Week 11 | Introduction to Heuristic and Metaheuristic Algorithms | | | Week 12 | Evolutionary Algorithms | | | Week 13 | Swarm Intelligence Algorithms | | | Week 14 | Recent Metaheuristic Algorithms | | | Week 15 | Engineering Applications of Metaheuristic Optimization | | | Week 16 | End-of term exam | | | |
| 1 | 1: Ders Notları (Lecture Notes)
2: Rao, Singiresu S. 2020; Engineering Optimization: Theory and Practice, 5th Edition, John Wiley & Sons, Inc., Hoboken, NJ, USA.
Print ISBN: 978-1119454717 Online ISBN: 978 1119454816 DOI: 10.1002/9781119454816 | | | |
| 1 | 3:Özturan, Aysun Tezel. 2019; Optimizasyon ve MATLAB Uygulamaları, Nobel Akademik Yayıncılık, Ankara.
ISBN: 978-605-7846-26-6 | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 3 | 30 | | Homework/Assignment/Term-paper | 10,11,12,13,14,15 | | 30 | 20 | | End-of-term exam | 16 | | 3 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 4 | 14 | 56 | | Arasınav için hazırlık | 3 | 8 | 24 | | Arasınav | 3 | 1 | 3 | | Ödev | 5 | 6 | 30 | | Dönem sonu sınavı için hazırlık | 7 | 8 | 56 | | Dönem sonu sınavı | 3 | 1 | 3 | | Total work load | | | 214 |
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