pyqlearning.annealingmodel.simulatedannealing package¶
Submodules¶
pyqlearning.annealingmodel.simulatedannealing.adaptive_simulated_annealing module¶
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class
pyqlearning.annealingmodel.simulatedannealing.adaptive_simulated_annealing.
AdaptiveSimulatedAnnealing
(cost_functionable, cycles_num=200, trials_per_cycle=50, accepted_sol_num=0.0, init_prob=0.7, final_prob=0.001, start_pos=0, move_range=3, tolerance_diff_e=None)[source]¶ Bases:
pyqlearning.annealingmodel.simulated_annealing.SimulatedAnnealing
Adaptive Simulated Annealing.
Adaptive Simulated Annealing, also known as the very fast simulated reannealing, is a very efficient version of simulated annealing.
References
- Bertsimas, D., & Tsitsiklis, J. (1993). Simulated annealing. Statistical science, 8(1), 10-15.
- Du, K. L., & Swamy, M. N. S. (2016). Search and optimization by metaheuristics. New York City: Springer.
- Mezard, M., & Montanari, A. (2009). Information, physics, and computation. Oxford University Press.
- Nallusamy, R., Duraiswamy, K., Dhanalaksmi, R., & Parthiban, P. (2009). Optimization of non-linear multiple traveling salesman problem using k-means clustering, shrink wrap algorithm and meta-heuristics. International Journal of Nonlinear Science, 8(4), 480-487.
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adaptive_set
(reannealing_per=50, thermostat=0.9, t_min=0.001, t_default=1.0)[source]¶ Init for Adaptive Simulated Annealing.
Parameters: - reannealing_per – How often will this model reanneals there per cycles.
- thermostat – Thermostat.
- t_min – The minimum temperature.
- t_default – The default temperature.