ASA

Adaptive Simulated Annealing (ASA) is a C-language code developed to statistically find the best global fit of a nonlinear constrained non-convex cost-function over a D-dimensional space. This algorithm permits an annealing schedule for ”temperature” T decreasing exponentially in annealing-time k, T = T_0 exp(-c k^1/D). The introduction of re-annealing also permits adaptation to changing sensitivities in the multi-dimensional parameter-space. This annealing schedule is faster than fast Cauchy annealing, where T = T_0/k, and much faster than Boltzmann annealing, where T = T_0/ln k. ASA has over 100 OPTIONS to provide robust tuning over many classes of nonlinear stochastic systems.


References in zbMATH (referenced in 68 articles , 1 standard article )

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  1. Russo, Vincenzo; Torri, Gabriele: Calibration of one-factor and two-factor hull-white models using swaptions (2019)
  2. Taig, Efrat; Ben-Shahar, Ohad: Gradient surfing: a new deterministic approach for low-dimensional global optimization (2019)
  3. Kabanikhin, Sergey; Krivorotko, Olga; Kashtanova, Victoriya: A combined numerical algorithm for reconstructing the mathematical model for tuberculosis transmission with control programs (2018)
  4. Zhu, Song-Ping; He, Xin-Jiang: A modified Black-Scholes pricing formula for European options with bounded underlying prices (2018)
  5. Ermakov, S. M.; Kulikov, D. V.; Leora, S. N.: Towards the analysis of the simulated annealing method in the multiextremal case (2017)
  6. Pál, László: Empirical study of the improved UNIRANDI local search method (2017)
  7. Valenzuela, Michael L.; Rozenblit, Jerzy W.: Learning using anti-training with sacrificial data (2016)
  8. Aguiar e O., Hime jun.; Petraglia, Antonio: Dimensional reduction in constrained global optimization on smooth manifolds (2015)
  9. Dhabal, Supriya; Venkateswaran, Palaniandavar: Two-dimensional IIR filter design using simulated annealing based particle swarm optimization (2014)
  10. Silva, Ricardo M. A.; Resende, Mauricio G. C.; Pardalos, Panos M.: Finding multiple roots of a box-constrained system of nonlinear equations with a biased random-key genetic algorithm (2014)
  11. Turgut, Oguz Emrah; Turgut, Mert Sinan; Coban, Mustafa Turhan: Chaotic quantum behaved particle swarm optimization algorithm for solving nonlinear system of equations (2014)
  12. Solonen, Antti: Proposal adaptation in simulated annealing for continuous optimization problems (2013)
  13. Cooren, Yann; Clerc, Maurice; Siarry, Patrick: MO-TRIBES, an adaptive multiobjective particle swarm optimization algorithm (2011)
  14. Ingber, Lester; Nunez, Paul L.: Neocortical dynamics at multiple scales: EEG standing waves, statistical mechanics, and physical analogs (2011)
  15. Xu, Jiuping; Zhou, Xiaoyang: Fuzzy-like multiple objective decision making (2011)
  16. Hilaire, Thibault; Chevrel, Philippe; Whidborne, James F.: Finite wordlength controller realisations using the specialised implicit form (2010)
  17. Tantar, Alexandru-Adrian; Melab, Nouredine; Talbi, El-Ghazali: A grid-based hybrid hierarchical genetic algorithm for protein structure prediction (2010)
  18. Chortaras, Alexandros; Stamou, Giorgos; Stafylopatis, Andreas: Definition and adaptation of weighted fuzzy logic programs (2009)
  19. Molvalioglu, Orcun; Zabinsky, Zelda B.; Kohn, Wolf: The interacting-particle algorithm with dynamic heating and cooling (2009)
  20. Oliveira, Hime A. jun.; Petraglia, Antonio; Petraglia, Mariane R.: Frequency domain FIR filter design using fuzzy adaptive simulated annealing (2009)

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