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| YZM4032 | Meta - Heuristic Optimization | 2+0+0 | ECTS:4 | | Year / Semester | Spring Semester | | Level of Course | First Cycle | | Status | Elective | | Department | DEPARTMENT of SOFTWARE ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 2 hours of lectures per week | | Lecturer | Prof. Dr. Hamdi Tolga KAHRAMAN | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | Teaching the basic elements and concepts of optimization, creating awareness about the design, development and operation of meta-heuristic search algorithms in harmony with nature, Modeling an engineering optimization problem with meta-heuristic search algorithms |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | Know the terminology for meta-heuristic search algorithms and meta-heuristic optimization issues. | 1,8 | 1 | | LO - 2 : | Explain the basic requirements that meta-heuristic search algorithms must meet. | 1,8 | 1 | | LO - 3 : | Explain the life cycle of meta- heuristic search algorithms. | 1,8 | 1 | | LO - 4 : | Know the elements of meta-heuristic optimization. | 1,8 | 1 | | LO - 5 : | Explain the process of experimental testing and verification of meta-heuristic optimization studies. | 1,8 | 1,6 | | LO - 6 : | Students can analyze the constrained or unconstrained continuous optimization problems in engineering with meta-heuristic search algorithms and compare the results with competing algorithms. | 1,8 | 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), LO : Learning Outcome | | |
| Introduction to Optimization, Engineering Optimization, Meta-Heuristic Search, Local Search and Diversity, Meta-Heuristic Algorithms, Application Project |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction to Optimization, Optimization Terminology and Definitions | | | Week 2 | Engineering Optimization, Optimization Type, Optimization Algorithms,
Research and Application Project | | | Week 3 | Creating Cost Function (Artificial Neural Network Example) | | | Week 4 | Meta-Heuristic Algorithm Test Problems, Measurement and comparison of search performances of meta-heuristic algorithms, Local search and diversity | | | Week 5 | Meta Heuristic Algorithms: Genetic Algorithm and Its Application
Ant Algorithm, Research and Application Project Control | | | Week 6 | Meta Heuristic Algorithms: Particle Swarm Optimization, Research and Application Project Control | | | Week 7 | Meta Heuristic Algorithms: Artificial Bee Colony Algorithm and Its Application | | | Week 8 | Meta Heuristic Algorithms: Graviational Search Algorithm and Its Application | | | Week 9 | Mid-term exam | | | Week 10 | Meta Heuristic Algorithms: Crow Search Algorithm and Its Application | | | Week 11 | Meta Heuristic Algorithms: Symbiotic Organism Search Algorithm and Its Application | | | Week 12 | Meta Heuristic Algorithms: Coyote Optimization Algorithm and Its Application | | | Week 13 | Research and Application Project Presentation | | | Week 14 | Research and Application Project Presentation | | | Week 15 | Research and Application Project Presentation | | | Week 16 | Final Exam | | | |
| 1 | Yang, Xin-She Engineering optimization an introduction with metaheuristic applications. John Wiley and Sons, 2010. | | | 2 | Luke, Sean. Essentials of metaheuristics. Vol. 113. Raleigh: Lulu, 2009. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | 24/11/2021 | 180 | 20 | | Project | 13 | 22/12/2021 | 180 | 30 | | End-of-term exam | 16 | 19/01/2022 | | 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 | 2 | 14 | 28 | | Sınıf dışı çalışma | 3 | 10 | 30 | | Arasınav için hazırlık | 3 | 5 | 15 | | Arasınav | 2 | 1 | 2 | | Proje | 2 | 10 | 20 | | Dönem sonu sınavı için hazırlık | 4 | 3 | 12 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 109 |
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