Optimization methods

1.

Subject title

Optimization methods

Методи за оптимизација

2.

Code

KN-Z-02

3.

Study program

Computer Science, Cloud Computing, Data science in computer science and engineering, Bioinformatics, Security, Cryptography and Coding, IT management, Eco-informatics, Inteligent Systems, Internet Technologies and cyber security, Software for embedded systems, Software Engineering, Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Software Engineering, Еducation with ICT, Statistics and Data Analytics, Statistics and Data Analytics,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Втор циклус

6.

Academic year / semester

5 / Зимски

7. Number of ECTS credits

6.0

8.

Instructor

проф. д-р Горан Велинов проф. д-р Весна Димитриевска Ристовска

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

The purpose of the course is to provide knowledge of SEO problems, Formulation of optimization problems and their classification, classic and heurist methods and algorithms for solving them as well as application in Informatics. After completing the course the student is expected to know how to formulates an optimization problem, to classify it according to theoretical aspects and choose adequate classic and/or the heurist method for its resolution.

11.

Subject content:

INTRODUCTION: Optimizational SEO Problem, Classification and Formulation Problems. Classic SEO: One-dimensional optimization, required Conditions, gradient method, Newton`s method, requiring global optimim; multi- Dimensional SEO: Optimum conditions, problem without restrictions, Linear restrictions, nonlinear restrictions. linear programming, square programming ;; nonlinear restrictions, penalties and barriers methods, Gradient-project methods, extended methods of Lagrange, other classic methods; Other types of SEO: Stochastic optimization, dynamic SEO. Heurist SEO: Basic Solving Concepts, Training Methods, taboo search, threshold methods; Population methods, evolutionary algorithms, genetic algorithms, evolutionary programming, optimization based The colony of ants (Ant colony), SEO Roy particles (Particle Swarm), Simulated Annealing.

12.

Learning methods:

-Консултации, Дискусии

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

30 + 30 + 60 + 30 + 30 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

30 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

30 hours

16.

Other forms of activities

16.1.

Project tasks

30 hours

16.2.

Independent tasks

60 hours

16.3.

Homework

30 hours

17.

Grading method

17.1.

Tests

0 points

17.2.

Seminar work / project (presentation: written and oral)

30 points

17.3.

Activities and learning

10 points

17.4.

Final exam

30 points

18.

Grading criteria (points / grade)

up to 50 points

5 (five) (F)

from 51 to 60 points

6 (six) (E)

from 61 to 70 points

7 (seven) (D)

from 71 to 80 points

8 (eight) (C)

from 81 to 90 points

9 (nine) (B)

from 91 to 100 points

10 (ten) (A)

19.

Condition for signature and taking final exam

нема

20.

Language of instruction

македонски

21.

Quality assurance method

механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature