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Metaheuristics Course

Metaheuristics Course


Metaheuristics Graduate Course

21 – 23 February 2018, Campus Ciutadella, Universitat Pompeu Fabra, Barcelona, Spain

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.


Christian BlumArtificial Intelligence Research Institute, IIIA-CSIC 

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

Jésica de ArmasUniversitat Pompeu Fabra & BGSMath

Belén Melián-BatistaUniversidad de La Laguna

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

Helena RamalhinhoDirector of the Business Analytics Research GroupUniversitat Pompeu Fabra & BGSMath

Fatos XhafaDepartament de Ciències de la Computació, Universitat Politècnica de Catalunya


  • Introduction to Combinatorial Optimization and Applications (Helena RamalhinhoPPTs VIDEO
  • Introduction to CPLEX – IBM (Sofiane Oussedik)
  • Iterated Local Search (Helena RamalhinhoPPTs VIDEO
  • Metaheuristics in Port Logistics (Belén Melián-BatistaPPTs VIDEO
  • Simheuristics: extending metaheuristics to cope with real-life uncertainty (Angel JuanVIDEO
  • Hybrid metaheuristics: Combining metaheuristics with other techniques for optimization (Christian BlumPPTs_1 PPTs_2 VIDEO
  • Machine Learning & Metaheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs (Jésica de ArmasPPTs VIDEO
  • Meta-heuristics for Cloud Optimisation (Fatos XhafaPPTs VIDEO
  • Biased Randomized Algorithms with Applications (Angel JuanVIDEO