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:

References in zbMATH (referenced in 109 articles , 1 standard article )

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  1. Moriguchi, Kai; Ueki, Tatsuhito; Saito, Masashi: Identification of effective implementations of simulated annealing for optimizing thinning schedules for single forest stands (2017)
  2. 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)
  3. Crisan, Dan; Míguez, Joaquín: Particle-kernel estimation of the filter density in state-space models (2014)
  4. Abdelkhalik, Ossama: Hidden genes genetic optimization for variable-size design space problems (2013)
  5. Brigante, Michele: Numerical algorithm for defect reconstruction in elastic media with a circular ultrasonic scanning (2013)
  6. Cassioli, Andrea; Izzo, Dario; Di Lorenzo, David; Locatelli, Marco; Schoen, Fabio: Global optimization approaches for optimal trajectory planning (2013)
  7. Solonen, Antti: Proposal adaptation in simulated annealing for continuous optimization problems (2013)
  8. Zhou, Enlu; Chen, Xi: Sequential Monte Carlo simulated annealing (2013)
  9. Ali, M.M.; Gabere, M.N.; Zhu, Wenxing: A derivative-free variant called DFSA of Dekkers and Aarts’ continuous simulated annealing algorithm (2012)
  10. Fujii, Sae; Uchiyama, Akira; Umedu, Takaaki; Yamaguchi, Hirozumi; Higashino, Teruo: Trajectory estimation algorithm for mobile nodes using encounter information and geographical information (2012) ioport
  11. Kosmas, O.T.; Vlachos, D.S.: Simulated annealing for optimal ship routing (2012) ioport
  12. Cai, Weiwei; Ewing, David J.; Ma, Lin: Investigation of temperature parallel simulated annealing for optimizing continuous functions with application to hyperspectral tomography (2011)
  13. Andreasen, Martin Møller: How to maximize the likelihood function for a DSGE model (2010)
  14. Brigante, M.; Sumbatyan, M.A.: An efficient numerical algorithm for crack reconstruction in elastic media by the circular US scanning (2010)
  15. Cai, Weiwei; Ma, Lin: Applications of critical temperature in minimizing functions of continuous variables with simulated annealing algorithm (2010)
  16. El Mouatasim, Abdelkrim: Two-phase generalized reduced gradient method for constrained global optimization (2010)
  17. Mahlke, D.; Martin, A.; Moritz, S.: A mixed integer approach for time-dependent gas network optimization (2010)
  18. Özgan, Ercan; Saruhan, Hamit: Modeling of asphalt concrete via simulated annealing (2010)
  19. Ranjbar, Mostafa; Hardtke, Hans-Jürgen; Fritze, Denny; Marburg, Steffen: Finding the best design within limited time: a comparative case study on methods for optimization in structural acoustics (2010)
  20. Ruciński, M.; Izzo, D.; Biscani, F.: On the impact of the migration topology on the island model (2010)

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