pyqlearning.annealingmodel.simulatedannealing package


pyqlearning.annealingmodel.simulatedannealing.adaptive_simulated_annealing module

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.


  • 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.
adaptive_set(reannealing_per=50, thermostat=0.9, t_min=0.001, t_default=1.0)[source]

Init for Adaptive Simulated Annealing.

  • reannealing_per – How often will this model reanneals there per cycles.
  • thermostat – Thermostat.
  • t_min – The minimum temperature.
  • t_default – The default temperature.

Change temperature.


Parameters:t – Now temperature.
Returns:Next temperature.

Module contents