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İzmir Ekonomi Üniversitesi

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    GRADUATE SCHOOL

    Ph.D. In Electrical-Electronics Engineering

    IES 538 | Course Introduction and Application Information

    Course Name
    Nature Inspired Optimization
    Code
    Semester
    Theory
    (hour/week)
    Application/Lab
    (hour/week)
    Local Credits
    ECTS
    IES 538
    Fall/Spring
    3
    0
    3
    7.5

    Prerequisites
    None
    Course Language
    English
    Course Type
    Elective
    Course Level
    Second Cycle
    Mode of Delivery -
    Teaching Methods and Techniques of the Course -
    National Occupation Classification -
    Course Coordinator -
    Course Lecturer(s) -
    Assistant(s) -
    Course Objectives The purpose of this course is to introduce natureinspired optimisation techniques and show how these techniques can be used.
    Learning Outcomes

    The students who succeeded in this course;

    • Explain how natureinspired optimisation techniques fit within the context of established optimisation theory
    • Apply a range of natureinspired algorithms to various realvalued and combinatorial optimisation problems
    • Design and adapt natureinspired algorithms to novel optimisation problems
    • Describe the appropriate underlying theory and discuss its current limitations
    Course Description This course introduces a range of natureinspired algorithms for both realvalued and combinatorial optimisation. Examples of such algorithms include: Evolutionary Algorithms, Ant Colony Algorithms, Simulated Annealing, Tabu Search. The study of these techniques and the problems for which they are designed will take place within the broader context of established optimisation theory. Such theory as currently exists for the new techniques will also be presented.

     



    Course Category

    		Core Courses
    Major Area Courses
    Supportive Courses
    Media and Management Skills Courses
    Transferable Skill Courses

     

    WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

    Week Subjects Related Preparation Learning Outcome
    1 Combinatorial optimisation – introduction to problems and algorithms / Kombinatoryel optimization –problem ve algoritmalara giriş
    2 Local search algorithms / Yerel arama algoritmaları
    3 Simulated annealing / Benzetilmiş Tavlama
    4 Genetic algorithms / Genetik Algoritmalar
    5 Ant colony optimisation / Karınca Kolonisi Algoritması
    6 Tabu search /Tabu Araması
    7 Hybrid methods / Hibrid Yöntemler
    8 Hybrid methods / Hibrid Yöntemler
    9 Real valued optimization / Gerçek değerli optimizasyon
    10 Evolutionary strategies / Evrimsel Stratejiler
    11 Evolutionary strategies / Evrimsel Stratejiler
    12 Gray code algorithms /Gri kod algoritmalar
    13 CHC / CHC
    14 No Free Lunch / No Free Lunch Teoremi
    15 Review and presentations / Tekrar ve sunumlar
    16 Review of the Semester  

     

    Course Notes/Textbooks Yukarıda belirtilen kitap ve ders yansıları / The textbook referenced above and course slides
    Suggested Readings/Materials Modern Heuristic Techniques for Combinatorial Problems C Reeves McGrawHill, 1995 How To Solve It Z Michalewicz & D B Fogel Springer, 2000 Stochastic Local Search H Hoos & T Stuzle Elsevier, 2005 New Ideas in Optimization D Corne, M Dorigo & F Glover McGrawHill, 1999İlgili Araştırma Makaleleri / Related Research Papers

     

    EVALUATION SYSTEM

    Semester Activities Number Weigthing
    Participation
    5
    Laboratory / Application
    Field Work
    Quizzes / Studio Critiques
    Portfolio
    Homework / Assignments
    5
    20
    Presentation / Jury
    Project
    1
    35
    Seminar / Workshop
    Oral Exams
    Midterm
    1
    20
    Final Exam
    1
    20
    Total

    Weighting of Semester Activities on the Final Grade
    80
    Weighting of End-of-Semester Activities on the Final Grade
    40
    Total

    ECTS / WORKLOAD TABLE

    Semester Activities Number Duration (Hours) Workload
    Theoretical Course Hours
    (Including exam week: 16 x total hours)
    16
    3
    48
    Laboratory / Application Hours
    (Including exam week: '.16.' x total hours)
    16
    0
    Study Hours Out of Class
    15
    4
    60
    Field Work
    0
    Quizzes / Studio Critiques
    0
    Portfolio
    0
    Homework / Assignments
    5
    8
    40
    Presentation / Jury
    10
    0
    Project
    1
    40
    40
    Seminar / Workshop
    0
    Oral Exam
    0
    Midterms
    1
    20
    20
    Final Exam
    1
    22
    22
        Total
    230

     

    COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

    #
    		 PC Sub Program Competencies/Outcomes
    * Contribution Level
    1
    2
    3
    4
    5
    1 Accesses information in breadth and depth by conducting scientific research in Electrical and Electronics Engineering; evaluates, interprets and applies information.
    -
    -
    -
    -
    -
    2 Is well-informed about contemporary techniques and methods used in Electrical and Electronics Engineering and their limitations.
    -
    -
    -
    -
    -
    3 Uses scientific methods to complete and apply information from uncertain, limited or incomplete data; can combine and use information from different disciplines. Knows and applies the research methods in studies of the area with a high level of skill.
    -
    -
    -
    -
    -
    4 Is informed about new and upcoming applications in the field and learns them whenever necessary.
    -
    -
    -
    -
    -
    5 Defines and formulates problems related to Electrical and Electronics Engineering, develops methods to solve them and uses progressive methods in solutions. Can independently realize novel studies that bring innovation to the field, or methods, or design, or known methods.
    -
    -
    -
    -
    -
    6 Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs.
    -
    -
    -
    -
    -
    7 Designs and implements studies based on theory, experiments and modeling; analyses and resolves the complex problems that arise in this process. Performs critical analysis, synthesis and evaluation of new and complex ideas.
    -
    -
    -
    -
    -
    8 Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations; can work independently and takes responsibility.
    -
    -
    -
    -
    -
    9 Engages in written and oral communication at least in Level C1 of the European Language Portfolio Global Scale.
    -
    -
    -
    -
    -
    10 Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.
    -
    -
    -
    -
    -
    11 Evaluates the results of scientific, technological and engineering research and development activities in terms of the social, environmental, health, safety and legal aspects. Examines social relations and norms related to the field, and develops and makes attempts to change them if necessary. Knows their project management and business applications, and is aware of their limitations in Electrical and Electronics Engineering applications.
    -
    -
    -
    -
    -
    12 Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity. Adheres to the principles of research and publication ethics.
    -
    -
    -
    -
    -

    *1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

     

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