HEURISTICS METHODS for OPTIMIZATION

AIM

It aims to improve current application and analysis skills with heuristic methods, and to apply heuristic methods such as simulated annealing, genetic algorithms and Tabu search.

CONTENT

This course contains; Introduction to the Course ,Introduction to Heuristic Methods,Simulated Annealing Algorithm,Genetic Algorithms,Evolutionary Strategies,Tabu Search,Ant Colony,Particle Surround Optimization,Hybrid Methods,Multi-objective Optimization,Current Optimization Applications,Analysis of Current Applications-1,Analysis of Current Applications-2,Analysis of Current Applications-3.

1. 1

   Students apply simulating annealing.

   Taught by: Discussion Method, Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework

2. 2

   Students Gain knowledge of what kind of problems Genetic Algorithm methods can be used in and how they can be applied.

   Taught by: Discussion Method, Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework

3. 3

   Students will be able to apply Tabu search method to related problems.

   Taught by: Discussion Method, Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework

4. 4

   The student applies the Ant Colony method to related problems.

   Taught by: Discussion Method, Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework

WEEKLY PLAN

1. WEEK 1

   Introduction to the Course

2. WEEK 2

   Introduction to Heuristic Methods

3. WEEK 3

   Simulated Annealing Algorithm

4. WEEK 4

   Genetic Algorithms

5. WEEK 5

   Evolutionary Strategies

6. WEEK 6

   Tabu Search

7. WEEK 7

   Ant Colony

8. WEEK 8

   Particle Surround Optimization

9. WEEK 9

   Hybrid Methods

10. WEEK 10

    Multi-objective Optimization

11. WEEK 11

    Current Optimization Applications

12. WEEK 12

    Analysis of Current Applications-1

13. WEEK 13

    Analysis of Current Applications-2

14. WEEK 14

    Analysis of Current Applications-3

ASSESSMENT

• Rate of Midterm Exam to Success30%
• Rate of Final Exam to Success70%

WORKLOAD

READING

• Metaheuristics for Hard Optimization: Methods and Case Studies, Johann Dréo , Patrick Siarry , Alain Pétrowski , Eric Taillard

TEACHING STAFF

• Lect.Dr. Esin TETİKCOORDINATOR
• Assist.Prof. Rüçhan Melisa DENİZ ÖZGEN