1. Introduction to the theory of complex optimization: computational complexity, combinatorial problems, NP-hard problems (K1-K4).
2. Evolutionary algorithms I (K1-K4).
3. Evolutionary algorithms II (K1-K4).
4. Simulated annealing (K1-K4).
5. Ant colony and bee colony algorithms (K1-K4).
6. Particle swarm optimization (K1-K4).
7. Hybrid optimization methods (K1-K4).
8. Test (1 hour).
1. Introduction to the workshops: organization, set up of work teams, assignment of topics. Introduction to available software tools to be used for solving optimization tasks (S6).
2. Presentation of literature review on selected optimization methods and their real-world applications (S9).
3. Problem definition, presentation of projects concepts proposed by every work team (S1, S2, S3, S5, S7).
4. Mastering software used for solving the selected optimization task (S6).
5. Practical realization of the projects, discussion (S2, S5, S6, S8, S9, S10).
6. Presentation of the project reports and obtained results by every work team
(S8, S9, S10).
Individual activity: 70 hours.