1. Mathematical optimization, linear & integer programming, Simplex method.
2. Metaheuristics, evolutionary algorithms for optimization.
3. Genetic algorithms.
4. Ant colony optimization.
5. Particle swarm optimization.
6. Simulated annealing.
7. Harmony search.
8. Differential evolution.
9. Machine learning, artificial neural networks.
10. Fuzzy systems.
11. Multi-criteria optimization, analytic hierarchy process.
12. Applications and case studies.
Optimization Methods
| Code | GPOL_R_16103 |
|---|---|
| Instructor | CHASIAKOS ATHANASIOS |
| eclass | https://eclass.upatras.gr/courses/CIV1756/ |
| Teaching Hours | 3 |
| Mandatory/Optional | Elective |
| Credits ECTS | 7,5 |
| COURCE OUTLINE | https://www.civil.upatras.gr/wp-content/uploads/2021/11/GPOL_R_16103-Optimization-methods-EN.docx |
By the end of this course, the graduate student will be able to:
- Design and develop mathematical and computational optimization models related to a number of applications in the disciplines of civil engineering, project and operations management, and operations research.
- Implement optimization models in software for problem solution.
- Evaluate comparatively alternative algorithms and optimization tools in relation to the problem under consideration.
- Create a framework of solutions to support decisions for the problem under consideration.
