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 77 articles , 1 standard article )

Showing results 41 to 60 of 77.
Sorted by year (citations)
  1. Du, Juan; Tang, Songyuan; Jiang, Tianzi; Lu, Zhensu: Intensity-based robust similarity for multimodal image registration (2006)
  2. Miettinen, Kaisa; Mäkelä, Marko M.; Maaranen, Heikki: Efficient hybrid methods for global continuous optimization based on simulated annealing (2006)
  3. Xavier-de-Souza, Samuel; Suykens, Johan A. K.; Vandewalle, Joos: Learning of spatiotemporal behaviour in cellular neural networks (2006)
  4. Ye, Hong; Lin, Zhiping: Speed-up simulated annealing by parallel coordinates (2006)
  5. Blum, Christian; Roli, Andrea; Alba, Enrique: An introduction to metaheuristic techniques (2005)
  6. Kim, Kwiseon; Graf, Peter A.; Jones, Wesley B.: A genetic algorithm based inverse band structure method for semiconductor alloys (2005)
  7. Zheng, R. T.; Ngo, N. Q.; Shum, P.; Tjin, S. C.; Binh, L. N.: A staged continuous tabu search algorithm for the global optimization and its applications to the design of fiber Bragg gratings (2005)
  8. de Vicente, Juan; Lanchares, Juan; Hermida, Román: Placement by thermodynamic simulated annealing (2003)
  9. Duch, Włodzisław; Grudziński, Karol: Meta-learning via search combined with parameter optimization (2002)
  10. Locatelli, Marco: Simulated annealing algorithms for continuous global optimization. (2002)
  11. Phan, Vinhthuy; Sumazin, Pavel; Skiena, Steven: A time-sensitive system for black-box combinatorial optimization (2002)
  12. Locatelli, M.: Convergence and first hitting time of simulated annealing algorithms for continuous global optimization (2001)
  13. Onbasoglu, Esin; Özdamar, Linet: Parallel simulated annealing algorithms in global optimization (2001)
  14. Özdamar, Linet; Demirhan, Melek: Comparison of partition evaluation measures in an adaptive partitioning algorithm for global optimization (2001)
  15. Pachai, Chahin; Zhu, Yue Min; Guttmann, Charles R. G.; Kikinis, Ron; Jolesz, Ferenc A.; Gimenez, Gérard; Froment, Jean-Claude; Confavreux, Christian; Warfield, Simon K.: Unsupervised and adaptive segmentation of multispectral 3D magnetic resonance images of human brain: A generic approach (2001)
  16. Randall, Marcus; Abramson, David: A general meta-heuristic based solver for combinatorial optimisation problems (2001)
  17. Shang, Yi; Li, Longzhuang; Wah, Benjamin: Optimization design of biorthogonal filter banks for image compression (2001)
  18. Steinberg, L.: Searching stochastically generated multi-abstraction-level design spaces (2001)
  19. Voß, Stefan: Meta-heuristics: The state of the art (2001)
  20. Duch, Włodzisław: Similarity-based methods: A general framework for classification, approximation and association (2000)