M.SC. in Computer Engineering (Without Thesis)

MATH 654 | Course Introduction and Application Information

Course Name

Discrete Optimization and Heuristic Methods

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
MATH 654	Fall/Spring	3	0	3	7.5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	Third Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	-
National Occupation Classification	-
Course Coordinator	-
Course Lecturer(s)	Doç. Dr. Gözde YAZGI TÜTÜNCÜ
Assistant(s)	Araş. Gör. Dr. Ayşe BELER Araş. Gör. Merve KAHRAMAN ARİMAN

Course Objectives

In this graduate course we introduce the modern heuristic optimization algorithms for solving discrete optimization problems. The course begins with a classification of the optimization problems and the definition of the primary concepts such as discrete and continuous search domains, multiobjective optimization, dynamic optimization, global optimization, stochastic optimization, swarm intelligence and etc. Then some of the wellknown heuristic methods such as Evolutionary Algorithms, Ant Colony Optimization, Simulated Annealing, Tabu Search, Particle Swarm Optimization, etc. are introduced in detail including the basic and original algorithms, characteristics, adaptation to constrained and multiobjective problems, parallelization and successful applications

Learning Outcomes

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	will be able to analyze discrete and continuous search domains.
2	will be able to analyze multiobjective optimization, dynamic optimization, global optimization, stochastic optimization.
3	will be able to apply Evolutionary Algorithms, Ant Colony Optimization method.
4	will be able to schedule using Simulated Annealing and Tabu Search.
5	will be able to apply Particle Swarm Optimization method.
6	will be able to introduce new heuristic methods.

Course Description

This course aims to cover the classification of the optimization problems and wellknown heuristic methods.

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	Definition of an Optimization Problem and Feasibility Problem	“How to solve it: modern heuristics” By Zbigniew Michalewicz, David B. Fogel, Ed.2, 2004, Springer.
2	Classification of the Optimization Problems	“How to solve it: modern heuristics” By Zbigniew Michalewicz, David B. Fogel, Ed.2, 2004, Springer.
3	Classification of the Optimization Techniques	“How to solve it: modern heuristics” By Zbigniew Michalewicz, David B. Fogel, Ed.2, 2004, Springer.
4	Overview of Classical Optimization Techniques	“How to solve it: modern heuristics” By Zbigniew Michalewicz, David B. Fogel, Ed.2, 2004, Springer.
5	An Overview of Heuristic Optimization Algorithms.	“How to solve it: modern heuristics” By Zbigniew Michalewicz, David B. Fogel, Ed.2, 2004, Springer.
6	Neighborhood Search.	“How to solve it: modern heuristics” By Zbigniew Michalewicz, David B. Fogel, Ed.2, 2004, Springer.
7	Hill Climbing Methods	“How to solve it: modern heuristics” By Zbigniew Michalewicz, David B. Fogel, Ed.2, 2004, Springer.
8	Randomrestart hill climbing	“How to solve it: modern heuristics” By Zbigniew Michalewicz, David B. Fogel, Ed.2, 2004, Springer.
9	Greedy Algorithms.	“How to solve it: modern heuristics” By Zbigniew Michalewicz, David B. Fogel, Ed.2, 2004, Springer.
10	Simulated Annealing	“How to solve it: modern heuristics” By Zbigniew Michalewicz, David B. Fogel, Ed.2, 2004, Springer.
11	Tabu Search	“How to solve it: modern heuristics” By Zbigniew Michalewicz, David B. Fogel, Ed.2, 2004, Springer.
12	Evolutionary Algorithms.	“How to solve it: modern heuristics” By Zbigniew Michalewicz, David B. Fogel, Ed.2, 2004, Springer.
13	Ant Colony Optimization.	“How to solve it: modern heuristics” By Zbigniew Michalewicz, David B. Fogel, Ed.2, 2004, Springer.
14	Bees algorithm	“How to solve it: modern heuristics” By Zbigniew Michalewicz, David B. Fogel, Ed.2, 2004, Springer.
15	Particle Swarm Optimization.	“How to solve it: modern heuristics” By Zbigniew Michalewicz, David B. Fogel, Ed.2, 2004, Springer.
16	Review of the Semester

Course Notes/Textbooks	The extracts above and exercises will be given.
Suggested Readings/Materials	Rao, S.S. (1984). Optimization Theory and Application. Wiley Eastern Ltd., New Delhi

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO 1	LO 2	LO 3	LO 4	LO 5	LO 6
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury	1	10
Project	1	20
Seminar / Workshop
Oral Exams
Midterm	1	30
Final Exam	1	40
Total

Weighting of Semester Activities on the Final Grade	3	60
Weighting of End-of-Semester Activities on the Final Grade	1	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	16	5	80
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments			0
Presentation / Jury	1	10	10
Project	1	15	15
Seminar / Workshop			0
Oral Exam			0
Midterms	1	32	32
Final Exam	1	40	40
		Total	225

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Accesses information in breadth and depth by conducting scientific research in Computer Engineering, evaluates, interprets and applies information.		-	-	-	X	-
2	Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.		-	-	X	-	-
3	Uses scientific methods to complete and apply information from uncertain, limited or incomplete data, can combine and use information from different disciplines.		-	-	-	X	-
4	Is informed about new and upcoming applications in the field and learns them whenever necessary.		-	-	-	-	X
5	Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.		-	-	-	-	X
6	Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs.		-	-	-	X	-
7	Designs and implements studies based on theory, experiments and modelling, analyses and resolves the complex problems that arise in this process.		-	-	-	X	-
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.		-	-	X	-	-
9	Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.		-	-	X	-	-
10	Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.		-	-	X	-	-
11	Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications.		-	-	X	-	-
12	Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.		-	X	-	-	-