SIMANN

Simulated annealing is a global optimization method that distinguishes between different local optima. Starting from an initial point, the algorithm takes a step and the function is evaluated. When minimizing afunction, any downhill step is accepted and the process repeats from this new point. An uphill step may be accepted. Thus, it can escape from local optima. This uphill decision is made by the Metropolis criteria. As the optimization process proceeds, the length of the steps decline and the algorithm closes in on the global optimum. Since the algorithm makes very few assumptions regarding the function to be optimized, it is quite robust with respect to non-quadratic surfaces. The degree of robustness can be adjusted by the user. In fact, simulated annealing can be used as a local optimizer for difficult functions.


References in zbMATH (referenced in 13 articles , 1 standard article )

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  1. Ferreiro-Ferreiro, A. M.; García-Rodríguez, J. A.; López-Salas, J. G.; Escalante, C.; Castro, M. J.: Global optimization for data assimilation in landslide tsunami models (2020)
  2. Ferreiro, Ana M.; García-Rodríguez, José Antonio; Vázquez, Carlos; Costa e Silva, E.; Correia, A.: Parallel two-phase methods for global optimization on GPU (2019)
  3. Chua, K. C.; Ong, S. H.: Test of misspecification with application to negative binomial distribution (2013)
  4. Ajmi, Ahdi Noomen; El Montasser, Ghassen: Seasonal Bi-parameter smooth transition autoregressive model for the UK industrial production index (2012)
  5. Kurmann, André: VAR-based estimation of Euler equations with an application to New Keynesian pricing (2007)
  6. Kendrick, David A.; Mercado, P. Ruben; Amman, Hans M.: Computational economics. (2006)
  7. Xie, Dexuan; Singh, Suresh B.; Fluder, Eugene M.; Schlick, Tamar: Principal component analysis combined with truncated-Newton minimization for dimensionality reduction of chemical databases (2003)
  8. Wang, B.; Sung, K. K.; Ng, T. K.: The localized consistency principle for image matching under non-uniform illumination variation and affine distortion (2002)
  9. Topa, Giorgio: Social interactions, local spillovers and unemployment (2001)
  10. Nakatsuma, Teruo: Bayesian analysis of ARMA-GARCH models: a Markov chain sampling approach (2000)
  11. Nakatsuma, Teruo: A Markov-Chain sampling algorithm for GARCH models (1998)
  12. Goffe, William L.: SIMANN: a global optimization algorithm using simulated annealing (1996)
  13. Zeephongsekul, P.: Stackelberg strategy solution for optimal software release policies (1996)