Kocaeli University / European Credit Transfer System (ECTS)

Name of Lecturer(s)

Prof. Dr. Kerem KÜÇÜK
Assistant Prof. Dr. Radhwan Ali Abdulghani SALEH

Learning Outcomes of the Course Unit
Program Competencies-Learning Outcomes Relation

Learning Outcomes of the Course Unit

1) Comprehend the concepts of optimization theory
2) Understands different optimization algorithms and grasps how they work.
3) Comprehend constraint optimization
4) Evaluates and analyzes optimization solutions and determines their effectiveness.
5) Applies optimization techniques to real-world problems and understands how these techniques can be practically utilized.

Program Competencies-Learning Outcomes Relation

		Program Competencies
		1	2	3	4	5	6	7	8	9
Learning Outcomes
	1	High	High	High	High	High	High	High	High	Low
	2	Middle	High	High	High	High	High	High	High	Low
	3	Middle	High	High	High	High	High	High	High	Low
	4	Middle	High	High	High	High	High	High	High	Low
	5	High	High	High	High	High	High	High	High	Low

Mode of Delivery

Face to Face

Prerequisites and Co-Requisites

None

Recommended Optional Programme Components

Not Required

Course Contents

This course equips candidates with in-depth knowledge on the classification of analitical optimization and techniques, unlimited, linear limited, nonlinear limited optimization, lagrange multiplier method, Kuhn-Tucker rules, punishment functions, linear, square and unlinear programing, engineering applications, dynamic optimization and Heuristic optimization.

Weekly Schedule

1) What is optimization? Types of optimization problems (continuous, discrete, constrained, unconstrained) Applications of optimization in real-world problems
2) What are metaheuristic algorithms? Exploration vs. exploitation Population-based vs. trajectory-based methods Advantages and limitations of metaheuristics
3) Swarm intelligence concepts PSO algorithm: velocity and position updates Parameters: inertia weight, cognitive and social factors
4) PSO Coding and Practice
5) Inspiration from honeybee foraging behavior Employed, onlooker, and scout bees Applications in optimization
6) Implement ABC for a benchmark problem
7) Combining metaheuristics Advantages of hybridization Case studies of hybrid algorithms
8) Inspiration from grey wolf social hierarchy and hunting behavior Alpha, beta, delta, and omega wolves Mathematical modeling of hunting and searching
9) Implement GWO for a benchmark problem
10) Pareto optimality and dominance Multi-objective metaheuristics Applications in real-world problems
11) PSO for Multi-objective real-world problems
12) Evaluation of course project
13) Ders projesinin değerlendirilmesi
14) Evaluation of course project
15) Mid-term exam
16) Final Exam

Planned Learning Activities and Teaching Methods

Assessment Methods and Criteria

Contribution of Semester Studies to Course Grade

50%

		Number	Percentage
Semester Studies	Midterm Examination	1	50%
Semester Studies	Project	1	50%

Contribution of Final Examination to Course Grade

50%

Total

100%

Language of Instruction

Turkish

Work Placement(s)