Algorithms simulated annealing

Note The segmentation operation yields a near-optimal estimate x that may be used as initialization point for an optimization algoritlim that has to find out the global minimum of the criterion /(.). Because of its nonlinear nature, we prefer to minimize it by using a stochastic optimization algorithm (a version of the Simulated Annealing algorithm [3]). 

Glover, LK., A New Simulated Annealing Algorithm for Standard Cell Placement , proc. IEEE Int. Conf. On Computer-Aided Design, Santa Clara, 378-380 (1986). 

Network Clustering Using Kernighan-Li and Simulated Annealing Algorithms... 

The simulated annealing algorithm (37) for the partitional clustering as we required in this work was designed based on the following problem formation. 

Let us consider an isotropic porous medium under the reconstruction described by a pore phase function fgk r) in the /cth iteration step of the simulated annealing algorithm and let the actual statistical characteristics of this phase function, i.e., the two-point correlation function, be Rgk u). The distance of from the target morphological characteristics Rgtarset(u) of the... 

The simulated annealing algorithm typically starts from the random phase function ff r) having the required porosity s = In the Arth iteration step,... 

Fig. 4. Simulated annealing algorithm of the cube consisting of 65 x 65 x 65 voxels starting from random initial condition (left), after 14 x 106 iterations (middle) and after 17 x 106 iterations (right). The two-point correlation function S u) = R(m)( — 2) + 2 is compared with the target correlation function (CF) during the reconstruction.

L. Goldstein, Mean Square Rates of Convergence in the Continuous Time Simulated Annealing Algorithm on Rd, (preprint, 1985)... 

Simulation of Adsorption in 3-D Reconstructed Mesoporous Materials by a Simulated Annealing Algorithm... 

Simulation of adsorption in 3-D reconstructed mesoporous materials by a simulated annealing algorithm... 

The basic simulated annealing algorithm does not require much more than a dozen lines of code (not including evaluation of the objective function) and the optimization constraints are easily handled by rejecting candidate points that violate them. 

Prescription of different fimctional dependencies of p, incorporation of different stretchings for Cerent variables, various recipes for pseudorandom step selection, and specifications of various termination criteria ensure that the basic simulated annealing algorithm can be modified to meet special requirements of most applications. 

This computational approach of finding the optimal alignment for the Kalman filter resolution of the overlapped shifted spectra by the simulated annealing algorithm has been tested on simulated overlapped spectra obtained by linear combination of Gaussian-Lorentzian curves, synthetically generated using the mathematical model described by Eqn. (9)... 

Partitioning is most appropriate when one is only interested in the subsets or clusters, while hierarchical decomposition is most applicable when one seeks to show similarity relationships between clusters. Section 2.1 formalizes the combinatorics of the partitional strategy and Section 2.2 does the same for hierarchical methods. The formulations we derive here provide the basis for the application of the simulated annealing algorithm to the underl5dng optimization problem as we show in Section 3. 

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