Simulated Annealing (SA)¶
- class pypop7.optimizers.sa.sa.SA(problem, options)¶
Simulated Annealing (SA).
This is the abstract class for all SA classes. Please use any of its instantiated subclasses to optimize the black-box problem at hand.
Note
- Parameters:
problem (dict) –
- problem arguments with the following common settings (keys):
’fitness_function’ - objective function to be minimized (func),
’ndim_problem’ - number of dimensionality (int),
’upper_boundary’ - upper boundary of search range (array_like),
’lower_boundary’ - lower boundary of search range (array_like).
options (dict) –
- optimizer options with the following common settings (keys):
’max_function_evaluations’ - maximum of function evaluations (int, default: np.Inf),
’max_runtime’ - maximal runtime to be allowed (float, default: np.Inf),
’seed_rng’ - seed for random number generation needed to be explicitly set (int);
- and with the following particular settings (keys):
’temperature’ - annealing temperature (float),
’x’ - initial (starting) point (array_like).
- temperature¶
annealing temperature.
- Type:
float
- x¶
initial (starting) point.
- Type:
array_like
References
Bouttier, C. and Gavra, I., 2019. Convergence rate of a simulated annealing algorithm with noisy observations. Journal of Machine Learning Research, 20(1), pp.127-171. https://www.jmlr.org/papers/v20/16-588.html
Siarry, P., Berthiau, G., Durdin, F. and Haussy, J., 1997. Enhanced simulated annealing for globally minimizing functions of many-continuous variables. ACM Transactions on Mathematical Software, 23(2), pp.209-228. https://dl.acm.org/doi/abs/10.1145/264029.264043
Bertsimas, D. and Tsitsiklis, J., 1993. Simulated annealing. Statistical Science, 8(1), pp.10-15. https://tinyurl.com/yknunnpt
Corana, A., Marchesi, M., Martini, C. and Ridella, S., 1987. Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithm. ACM Transactions on Mathematical Software, 13(3), pp.262-280. https://dl.acm.org/doi/abs/10.1145/29380.29864 https://dl.acm.org/doi/10.1145/66888.356281
Kirkpatrick, S., Gelatt, C.D. and Vecchi, M.P., 1983. Optimization by simulated annealing. Science, 220(4598), pp.671-680. https://science.sciencemag.org/content/220/4598/671
Hastings, W.K., 1970. Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), pp.97-109. https://academic.oup.com/biomet/article/57/1/97/284580
Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H. and Teller, E., 1953. Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21(6), pp.1087-1092. https://aip.scitation.org/doi/abs/10.1063/1.1699114