Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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About the Program Educational Objectives Key Learning Outcomes Course Structure Diagram with Credits Field Qualifications Matrix of Course& Program Qualifications Matrix of Program Outcomes&Field Qualifications
  • Faculty of Engineering
  • Department of Industrial Engineering
  • Course Structure Diagram with Credits
  • Introduction to Metaheuristic Optimization
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications

Course Introduction Information

Code - Course Title ENM450 - Introduction to Metaheuristic Optimization
Course Type Area Elective Courses
Language of Instruction İngilizce
Laboratory + Practice 3+0
ECTS 6.0
Course Instructor(s) DOKTOR ÖĞRETİM ÜYESİ EMİNE AKYOL ÖZER
Mode of Delivery Face to face
Prerequisites ENM 104: Introduction to Computation and Programming for Industrial EngineeringENM 212: Integer Programming and Network Models
Courses Recomended ENM 104: Introduction to Computation and Programming for Industrial EngineeringENM 212: Integer Programming and Network Models
Recommended Reading List Handbook of MetaheuristicsEditors: Gendreau, Michel, Potvin, Jean-Yves
Assessment methods and criteria 2 Midterm Exam, 1 Project, 1 Final Exam
Work Placement
Sustainability Development Goals

Content

Weeks Topics
Week - 1 Necessity of Metaheuristic Optimization - Fundamentals, Models, Exact Solution Methods
Week - 2 Introduction to the Metaheuristic Optimization: NP hard models,necessity, fundamentals
Week - 3 Hill Climbing Algorithm
Week - 4 Single-Solution Based Metaheuristics
Week - 5 Simulated annealing algorithm
Week - 6 Single-Solution Based Metaheuristics Applications
Week - 7 Tabu search algorithm
Week - 8 Implementation of single-solution based algorithms and Industrial engineering applications
Week - 9 Evolutionary Algorithms - Fundamentals,
Week - 10 Genetic Algorithms
Week - 11 Application of Evolutionary Algorithms
Week - 12 Ant colony algorithms
Week - 13 Bee colony algorithms
Week - 14 Implementation of algorithms and Industrial engineering applications

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Discussion
  • Question & Answer
  • Team/Group Work
  • Proje Design/Management
  • Competences
  • Productive
  • Rational
  • Creative
  • Work in teams
  • Use time effectively
  • Concern for quality

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 30
Project 1 20
Final Exam 1 50
Toplam (%) 100
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