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19M074OOA - Optimization algorithms in engineering

Course specification
Course title Optimization algorithms in engineering
Acronym 19M074OOA
Study programme Electrical Engineering and Computing
Module
Type of study master academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
ESPB 6.0 Status elective
Condition None.
The goal Detailed overview of 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. Random search, systematic search, gradient method, traditional methods, simplex algorithms, genetic algorithm, simulated annealing, differential evolution and particle swarm optimization. Multicriteria optimization. Pareto front. Practical applications.
Contents of exercises Solving optimization problems using computer.
Literature
  1. Z. Michalewicz, D.B. Fogel, How to Solve It: Modern Heuristics, Springer; 2nd edition, 2004. (Original title)
  2. Xin-She Yang, Engineering Optimization: An Introduction with Metaheuristic Applications, Wiley, 2010. (Original title)
  3. 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