simannf90

Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithm From authors’ summary: A new global optimization algorithm for functions of continuous variables is presented, derived from the “simulated annealing” algorithm recently introduced in combinatorial optimization. The algorithm is essentially an iterative random search procedure with adaptive moves along the coordinate directions. It permits uphill moves under the control of a probabilistic criterion, thus tending to avoid the first local minima encountered. The new method proved to be more reliable than the others (the Nelder-Mead method and the adaptive random search method of the reviewer), being always able to find the optimum, or at least a point very close to it. It is quite costly in terms of function evaluations, but its cost can be predicted in advance, depending only slightly on the starting point. (Source: http://plato.asu.edu)


References in zbMATH (referenced in 114 articles , 1 standard article )

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  1. Gao, Siyang; Shi, Leyuan; Zhang, Zhengjun: A peak-over-threshold search method for global optimization (2018)
  2. McGree, J. M.: Developments of the total entropy utility function for the dual purpose of model discrimination and parameter estimation in Bayesian design (2017)
  3. Moriguchi, Kai; Ueki, Tatsuhito; Saito, Masashi: Identification of effective implementations of simulated annealing for optimizing thinning schedules for single forest stands (2017)
  4. McGree, James M.; Drovandi, C. C.; White, Gentry; Pettitt, A. N.: A pseudo-marginal sequential Monte Carlo algorithm for random effects models in Bayesian sequential design (2016)
  5. Crisan, Dan; Míguez, Joaquín: Particle-kernel estimation of the filter density in state-space models (2014)
  6. Abdelkhalik, Ossama: Hidden genes genetic optimization for variable-size design space problems (2013)
  7. Brigante, Michele: Numerical algorithm for defect reconstruction in elastic media with a circular ultrasonic scanning (2013)
  8. Cassioli, Andrea; Izzo, Dario; Di Lorenzo, David; Locatelli, Marco; Schoen, Fabio: Global optimization approaches for optimal trajectory planning (2013)
  9. Solonen, Antti: Proposal adaptation in simulated annealing for continuous optimization problems (2013)
  10. Zhou, Enlu; Chen, Xi: Sequential Monte Carlo simulated annealing (2013)
  11. Ali, M. M.; Gabere, M. N.; Zhu, Wenxing: A derivative-free variant called DFSA of Dekkers and Aarts’ continuous simulated annealing algorithm (2012)
  12. Fujii, Sae; Uchiyama, Akira; Umedu, Takaaki; Yamaguchi, Hirozumi; Higashino, Teruo: Trajectory estimation algorithm for mobile nodes using encounter information and geographical information (2012) ioport
  13. Kosmas, O. T.; Vlachos, D. S.: Simulated annealing for optimal ship routing (2012) ioport
  14. Cai, Weiwei; Ewing, David J.; Ma, Lin: Investigation of temperature parallel simulated annealing for optimizing continuous functions with application to hyperspectral tomography (2011)
  15. Denman, N. G.; Mcgree, J. M.; Eccleston, J. A.; Duffull, S. B.: Design of experiments for bivariate binary responses modelled by copula functions (2011)
  16. Andreasen, Martin Møller: How to maximize the likelihood function for a DSGE model (2010)
  17. Brigante, M.; Sumbatyan, M. A.: An efficient numerical algorithm for crack reconstruction in elastic media by the circular US scanning (2010)
  18. Cai, Weiwei; Ma, Lin: Applications of critical temperature in minimizing functions of continuous variables with simulated annealing algorithm (2010)
  19. El Mouatasim, Abdelkrim: Two-phase generalized reduced gradient method for constrained global optimization (2010)
  20. Lieli, Robert P.; White, Halbert: The construction of empirical credit scoring rules based on maximization principles (2010)

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