Hill climbing and simulated annealing are two fundamental search techniques integrating most artificial intelligence and machine learning courses curricula. These techniques serve as introduction to stochastic and probabilistic based metaheuristics. Simulated annealing can be considered a hill-climbing variant with a probabilistic decision. While simulated annealing is conceptually a simple algorithm, in practice it can be difficult to parameterize. In order to promote a good simulated annealing algorithm perception by students, a simulation experiment is reported here. Key implementation issues are addressed, both for minimization and maximization problems. Simulation results are presented.
CITATION STYLE
de Moura Oliveira, P. B., Pires, E. J. S., & Novais, P. (2017). Revisiting the simulated annealing algorithm from a teaching perspective. In Advances in Intelligent Systems and Computing (Vol. 527, pp. 718–727). Springer Verlag. https://doi.org/10.1007/978-3-319-47364-2_70
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