Information Package / Course Catalogue
Continuous Global Optimization
Course Code: MTK649
Course Type: Area Elective
Couse Group: Third Cycle (Doctorate Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 8
Objectives of the Course

The purpose of this course is to introduce and to understand the significance of global optimization in view of heuristics and metaheuristics. The course also aims to gain the ability of researching and improving heuristic approaches in this field.

Course Content

Overview of global optimization and the required mathematical preliminaries. Swarm intelligence based optimization methods, Physics based optimization algorithms, Bio-inspired computing and Chemistry based computing intelligence.

Name of Lecturer(s)
Learning Outcomes
1.Ability to understand the origin of heuristics and meta-heuristic optimization algorithms.
2.Ability to select the best alternative optimization method in the sense of a given objective function.
3.Ability to use the heuristics methods for optimization problems.
4.To be able to gain the skill of interpreting some interrelations among these concepts
5.To be able to use mathematical concepts in solving certain types of problems
Recommended or Required Reading
1.Xing, Bo, Gao, Wen-Jing, Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms, Intelligent Systems Reference Library Series, Vol. 62, ISBN: 978-3-319-34930-5, Springer International Publishing, 2014.
2.Sivanandam, S.N., Deepa, S. N, Introduction to Genetic Algorithms, ISBN: 978-3-642-09224-4, Springer-Verlag, 2008.
3.Price, Kenneth, Storn, Rainer M., Lampinen, Jouni A., Differential Evolution - A Practical Approach to Global Optimization, Springer-Verlag, 2005.
Weekly Detailed Course Contents
Week 1 - Theoretical
Overview of global optimization and the required mathematical preliminaries, classification of global optimization algorithms
Week 2 - Theoretical
Swarm Intelligence and Particle Swarm Optimization (PSO)
Week 3 - Theoretical
Some Variants of PSO
Week 4 - Theoretical
Imperialist Competitive Algorithm (ICA)
Week 5 - Theoretical
Some Variants of ICA (ICAAI, ICACI, GICA, EXPLICA)
Week 6 - Theoretical
Physics based Intelligence and Gravitational Search Algorithm (GSA)
Week 7 - Theoretical
Some Variants of GSA
Week 8 - Theoretical
Bio-inspired computing and Bacterial Foraging Algorithm (BFA)
Week 9 - Theoretical
Genetic Algorithms, MIDTERM EXAM
Week 10 - Theoretical
Differential Evolution Algorithm
Week 11 - Theoretical
Other heuristic methods, Artificial Bee Colony (ABC), Ant Colony (ACO) etc.
Week 12 - Theoretical
Other heuristic methods, Artificial Bee Colony (ABC), Ant Colony (ACO) etc.
Week 13 - Theoretical
Hybridization of global optimization methods
Week 14 - Final Exam
Applications of heuristic algorithms
Week 15 - Theoretical
FINAL EXAM
Assessment Methods and Criteria
Type of AssessmentCountPercent
Assignment1%5
Term Assignment1%5
Midterm Examination1%20
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140342
Assignment1066
Term Project1066
Individual Work140570
Midterm Examination130232
Final Examination142244
TOTAL WORKLOAD (hours)200
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
PÇ-12
PÇ-13
PÇ-14
PÇ-15
OÇ-1
4
4
4
4
3
4
4
2
5
OÇ-2
4
3
3
3
3
4
2
4
OÇ-3
4
4
4
4
4
4
2
4
OÇ-4
4
4
4
4
4
3
3
3
OÇ-5
4
4
4
4
4
3
3
4
Adnan Menderes University - Information Package / Course Catalogue
2026