26M071OOA - Optimization Algorithms in Engineering

Course specification
Course title		Optimization Algorithms in Engineering
Acronym		26M071OOA
Study programme		Electrical Engineering and Computing
Module		Applied Mathematics, Audio and Video Technologies, Biomedical and Nuclear Engineering, Computer Engineering and Informatics, Electronics and Digital Systems, Energy Efficiency, Information and Communication Technologies, Microwave Engineering, Nanoelectronics and Photonics, Power Systems - Networks and Systems, Power Systems - Renewable Energy Sources, Power Systems - Substations and Power Equipment, Signals and Systems, Software Engineering
Type of study		master academic studies
Lecturer (for classes)		professor PhD Dragan Olćan asst. professor PhD Jelena Dinkić
Lecturer/Associate (for practice)		professor PhD Dragan Olćan asst. professor PhD Jelena Dinkić
Lecturer/Associate (for OTC)		professor PhD Dragan Olćan asst. professor PhD Jelena Dinkić
ESPB		6.0	Status	elective
Condition		None.
The goal		Detailed overview of modern optimization algorithms used in engineering practice. Introduction to concepts of solving optimization problems in practical applications.
The outcome		Students will be able to apply outlined optimization algorithms for solving practical problems.
Contents
Contents of lectures		Terminology and theory of optimization problems. Classification of optimization problems and algorithms. Solving multicriteria optimization problems.
Contents of exercises		Solving optimization problems using computer.
Literature
Z. Michalewicz, D.B. Fogel, How to Solve It: Modern Heuristics, Springer; 2nd edition, 2004. (Original title) Xin-She Yang, Engineering Optimization: An Introduction with Metaheuristic Applications, Wiley, 2010. (Original title) D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Professional, 1989. (Original title)
Number of hours per week during the semester/trimester/year
Lectures	Exercises	OTC	Study and Research	Other classes
2	2	1
Methods of teaching		Lectures, coding, tests, homeworks and individual projects.
Knowledge score (maximum points 100)
Pre obligations		Points	Final exam	Points
Activites during lectures		60	Test paper	30
Practical lessons		10	Oral examination
Projects
Colloquia
Seminars