Nevşehir Hacı Bektaş Veli University Course Catalogue
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| Year/Semester of Study | 1 / Spring Semester | ||||
| Level of Course | 2nd Cycle Degree Programme | ||||
| Type of Course | Optional | ||||
| Department | ELECTRICAL AND ELECTRONICS ENGINEERING (MASTER) | ||||
| Pre-requisities and Co-requisites | None | ||||
| Mode of Delivery | Face to Face | ||||
| Teaching Period | 14 Weeks | ||||
| Name of Lecturer | SUAD BAŞBUĞ (suad@nevsehir.edu.tr) | ||||
| Name of Lecturer(s) | |||||
| Language of Instruction | Turkish | ||||
| Work Placement(s) | None | ||||
| Objectives of the Course | |||||
| Learning the use of metaheuristic algorithms in optimization processes. Understanding the basic metaheuristic algorithm parameters by considering single-solution and population-based techniques. Study the effect of randomness on the algorithms. To recognize and apply some commonly used metaheuristic algorithms. | |||||
| Learning Outcomes | PO | MME | |
| The students who succeeded in this course: | |||
| LO-1 | 74/5000 Understanding the use of algorithms and engineering Metaheuristic |
PO-1 Sufficient knowledge in mathematics, science and engineering related to their branches; the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems. PO-2 The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose. |
Examination |
| LO-2 | Understanding single-solution and population-based algorithms |
PO-2 The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose. PO-3 The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. (Realistic constraints and conditions include such issues as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, according to the nature of design.) PO-11 Information on the effects of engineering applications on health, environment and safety in the universal and social dimensions and the problems of the times; awareness of the legal consequences of engineering solutions. |
Examination |
| LO-3 | To be able to recognize commonly used algorithms and write their programs |
PO-1 Sufficient knowledge in mathematics, science and engineering related to their branches; the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems. PO-2 The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose. PO-11 Information on the effects of engineering applications on health, environment and safety in the universal and social dimensions and the problems of the times; awareness of the legal consequences of engineering solutions. |
Examination |
| PO: Programme Outcomes MME:Method of measurement & Evaluation |
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| Course Contents | ||
| Optimization Models Single Solution Based Metaheuristic Local Search and Neighborhood Objective Function and Analysis simulated annealing Algorithm Tabu Search Algorithm Guided Regional Search Population Based Metasezisiler Initial Population and Stopping Criteria Evolutionary Algorithms Genetic Algorithms Evolutionary Programming Differential Evolution Algorithm Bacterial Foraging Algorithm | ||
| Weekly Course Content | ||
| Week | Subject | Learning Activities and Teaching Methods |
| 1 | Optimization Models | Lecture, question and answer, discussion |
| 2 | Single Solution Based Metaheuristics | Lecture, question and answer, discussion |
| 3 | Local Search and Neighborhood | Lecture, question and answer, discussion |
| 4 | Objective Function and Analysis | Lecture, question and answer, discussion |
| 5 | Simulated Annealing algorithm | Lecture, question and answer, discussion |
| 6 | Tabu Search Algorithm | Lecture, question and answer, discussion |
| 7 | Guided Regional Search | Lecture, question and answer, discussion |
| 8 | mid-term exam | |
| 9 | Population Based Metaheuristics | Lecture, question and answer, discussion |
| 10 | Start Population and Stopping Criteria | Lecture, question and answer, discussion |
| 11 | Evolutionary Algorithms | Lecture, question and answer, discussion |
| 12 | Genetic Algorithms (Selection, sampling, mutation, recombination) | Lecture, question and answer, discussion |
| 13 | Evolutionary Programming | Lecture, question and answer, discussion |
| 14 | Differential Evolution Algorithm | Lecture, question and answer, discussion |
| 15 | Bacterial Foraging Algorithm | Lecture, question and answer, discussion |
| 16 | final exam | |
| Recommend Course Book / Supplementary Book/Reading | ||
| 1 | E.-G. Talbi, Metaheuristics: From Design to Implementation. John Wiley & Sons, 2009. | |
| 2 | S. Luke, Essentials of Metaheuristics. Morrisville, N.C.: lulu.com, 2013. | |
| Required Course instruments and materials | ||
| Course book, laptop computer, projector | ||
| Assessment Methods | |||
| Type of Assessment | Week | Hours | Weight(%) |
| mid-term exam | 8 | 1 | 40 |
| Other assessment methods | |||
| 1.Oral Examination | |||
| 2.Quiz | |||
| 3.Laboratory exam | |||
| 4.Presentation | |||
| 5.Report | |||
| 6.Workshop | |||
| 7.Performance Project | |||
| 8.Term Paper | |||
| 9.Project | |||
| final exam | 16 | 1 | 60 |
| Student Work Load | |||
| Type of Work | Weekly Hours | Number of Weeks | Work Load |
| Weekly Course Hours (Theoretical+Practice) | 3 | 14 | 42 |
| Outside Class | |||
| a) Reading | 3 | 11 | 33 |
| b) Search in internet/Library | 3 | 11 | 33 |
| c) Performance Project | 3 | 10 | 30 |
| d) Prepare a workshop/Presentation/Report | 0 | ||
| e) Term paper/Project | 3 | 10 | 30 |
| Oral Examination | 0 | ||
| Quiz | 0 | ||
| Laboratory exam | 0 | ||
| Own study for mid-term exam | 5 | 1 | 5 |
| mid-term exam | 1 | 1 | 1 |
| Own study for final exam | 5 | 1 | 5 |
| final exam | 1 | 1 | 1 |
| 0 | |||
| 0 | |||
| Total work load; | 180 | ||