Revisiting the Simulated Annealing Algorithm from a Teaching Perspective
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.
KeywordsSimulated annealing Meta-heuristics Artificial intelligence education
- 1.Russel, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Pearson Education, Upper Saddle River (2014)Google Scholar
- 4.Nandhini, M., Kanmani, S.: A survey of simulated annealing methodology for university course timetabling. Int. J. Recent Trends Eng. 1(2), 177–178 (2009)Google Scholar
- 10.Mirhosseini, S.H., Yarmohamadi, H., Kabudian, J.: MiGSA: a new simulated annealing algorithm with mixture distribution as generating function. In: 4th International Conference on Computer and Knowledge Engineering, pp. 455–461. IEEE (2014)Google Scholar
- 16.Shakouri, H.G., Shojaee, K., Behnam, M.T.: Investigation on the choice of the initial temperature in the simulated annealing: a Mushy State SA for TSP. In: 17th IEEE Mediterranean Conference on Control & Automation, Thessaloniki, Greece, pp. 1050–1055, 24–26 June 2009Google Scholar
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