The adaptive simulated annealing (ASA) algorithm [3] has been shown to be faster and more efficient than simulated annealing and genetic algorithms [4]. In this article we first outline some of the aspects of the method and specific computational details, and then review the application of the ASA method to biomolecular structure determination [15], specifically for Met-Enkephalin and a model of the poly(L-Alanine) system.
The ASA Method
For a system described by a cost function E({p i}), where all p i (i = 1,..., D) are parameters (variables) having ranges [A i , B i ], the ASA procedure to find the global optimum of ‘E’ contains the following elements.
Monte-Carlo Configurations
As the kth point is saved in a D-dimensional configuration space, the new point p k + 1 i is generated by:
where the random variables y i in [−1, 1] (non-uniform) are generated from a random number u i uniformly distributed in [0, 1], and the temperature T i associated with parameter p...
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Pachter, R., Wang, Z. (2001). Adaptive Simulated Annealing and its Application to Protein Folding . In: Floudas, C.A., Pardalos, P.M. (eds) Encyclopedia of Optimization. Springer, Boston, MA. https://doi.org/10.1007/0-306-48332-7_5
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DOI: https://doi.org/10.1007/0-306-48332-7_5
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