Metaheuristics Graduate Course

http://blogbarg.upf.edu/metaheuristics-course/

Metaheuristics are general high-level procedures that coordinate simple heuristics and rules to find high-quality solutions to difficult optimization problems. They are based on distinct paradigms and offer different mechanisms to go beyond the first solution obtained that cannot be improved by local search. They are frequently built upon a number of common building blocks such as greedy algorithms, randomization, neighborhoods and local search, reduced neighborhoods and candidate lists, intensification, diversification, path-relinking, and periodical restarts. Metaheuristics are among the most effective solution strategies for solving combinatorial optimization problems in practice and very frequently produce much better solutions than those obtained by the simple heuristics and rules they coordinate. They are designed to solve large-scale optimization problems that cannot be solved in reasonable processing time by the classic combinatorial optimization methods.

Lecturers

Christian Blum, Artificial Intelligence Research Institute, IIIA-CSIC 

Angel A. Juan, Internet Computing & Systems Optimization Research Group – IN3, Universitat Oberta de Catalunya

Jésica de Armas, Universitat Pompeu Fabra & BGSMath

Belén Melián-Batista, Universidad de La Laguna

Sofiane Oussedik, Technical Sales and Solutions Leader, IBM Analytics – Decision Optimization

Helena Ramalhinho, Director of the Business Analytics Research Group, Universitat Pompeu Fabra & BGSMath

Fatos Xhafa, Departament de Ciències de la Computació, Universitat Politècnica de Catalunya

Talks 

• Introduction to Combinatorial Optimization and Applications (Helena Ramalhinho) PPTs VIDEO
• Introduction to CPLEX – IBM (Sofiane Oussedik)
• Iterated Local Search (Helena Ramalhinho) PPTs VIDEO
• Metaheuristics in Port Logistics (Belén Melián-Batista) PPTs VIDEO
• Simheuristics: extending metaheuristics to cope with real-life uncertainty (Angel Juan) VIDEO
• Hybrid metaheuristics: Combining metaheuristics with other techniques for optimization (Christian Blum) PPTs_1 PPTs_2 VIDEO
• Machine Learning & Metaheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs (Jésica de Armas) PPTs VIDEO
• Meta-heuristics for Cloud Optimisation (Fatos Xhafa) PPTs VIDEO
• Biased Randomized Algorithms with Applications (Angel Juan) VIDEO