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Discipline Module 4 · Prescriptive Methods

Optimization Methods

Workload
15h
Professor Prof. Dr. Alexandre Xavier PhD in Electrical Engineering from UFMG, with postdoctoral studies at the Université de Technologie de Troyes.
Syllabus

Techniques for solving combinatorial optimization problems: classical heuristics and metaheuristics. Main metaheuristics: Simulated Annealing, Tabu Search, Iterated Local Search (ILS), Variable Neighborhood Search (VNS), Greedy Randomized Adaptive Search Procedures (GRASP), Genetic Algorithms, and Ant Colony Optimization.

Content
  • Techniques for solving combinatorial optimization problems: classical heuristics and metaheuristics
  • Main metaheuristics: Simulated Annealing, Tabu Search, Iterated Local Search (ILS), Variable Neighborhood Search (VNS), Greedy Randomized Adaptive Search Procedures (GRASP), Genetic Algorithms, and Ant Colony Optimization
Core Bibliography
  • Dréo, J., Pétrowski, A., Siarry, P., Taillard, E., Metaheuristics for hard optimization: Methods and Case Studies, Springer, 2006.
  • Aarts, E., Lenstra, J.K., Local Search in Combinatorial Optimization, Princeton University Press, 2003.
  • Feo, T. A., Resende, M. G. C., Greedy Randomized Adaptive Search Procedures, Journal of Global Optimization, Vol. 6, no 2, 1995.
Complementary Bibliography
  • Papadimitriou, C. H., Steiglitz, K., Combinatorial Optimization: Algorithms and Complexity, Dover, 1998.
  • Kirkpatrick, S. Gelatt Jr., C. D., Vecchi, M. P., Optimization by Simulated Annealing, Science 13, Vol. 220, 1983.
  • Glover, F., Tabu Search - Part I, ORSA Journal on Computing, Vol 1, no 3, 1989.
  • David E. Goldberg, D. E., Holland, J. H., Genetic Algorithms and Machine Learning, Machine Learning, Vol. 3, 1988.
  • Mladenovic, N., Hansen, P., Variable Neighborhood Search, Computers and Operations Research, Vol. 24, no 11, 1997.
  • Gaspar-Cunha, A., Takahashi, R., Antunes, C. H., Manual de Computação Evolutiva e Metaheurísticas, Editora UFMG.
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