1. Introduction. Definitions and formulation of the optimization problem.
2. Classic methods: Gradient based methods, Lagrange multipliers, Kuhn-Tucker conditions, Penalty methods.
3. Modern methods: Particle Swarm Optimization, Ant Colony Optimitzation, Genetic Algorithms. Metamodels.
4. Multiobjective optimization
Every proposed exercise should be delivered in a report including the procedure, results and conclusions.
The course work should reflect the theory presented in the course and respect the structure problem-solution method-results-conclusions.
Criteris específics de la nota «No Presentat»:
All the tasks which have some % in the note must be presented. Otherwise the final mark of the course will be Non-marked (NP).