Optimization Heuristics I+II

Learning Objectives:

After attending this course, students will be able to recognize technical-economic problems and to classify them regarding their complexity. They know the basic principles for the construction of heuristic optimization methods and have a sound knowledge of established heuristic optimization approaches. Students are able to independently apply heuristics to solve practical problems.

Contents:

• Complexity of optimization problems
• Heuristic solution methods
• Local search methods (Hill Climbing, Tabu Search, Simulated Annealing)
• Population based methods (Genetic Algorithms, Ant Algorithms, Particle Swarm Optimization)
• Constraint Propagation
• Shortened Enumeration Methods

Literature:

• Domschke, W. (1997): Logistik: Rundreisen und Touren, 4th ed.
• Glover, F., Kochenberger, G. A. (2003): Handbook of Metaheuristics
• Goldberg, D. E. (1989): Genetic Algorithms in Search, Optimization, and Machine Learning
• Hoos, H. H., Stützle, T. (2005): Stochastic Local Search – Foundations and Applications
• Michalewicz, Z. Fogel, D. B. (2004): How to Solve It: Modern Heuristics