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Abstract. This paper proposes a simulated annealing variant for optimization problems in which the solution quality can only be estimated by sampling from a random distribution. The aim is to [...]

We develop a population-based algorithm for the optimization of multiple, nonconvex, nondifferentiable, and possibly discontinuous objective functions. The algorithm employs Markov kernels, Hit-and-Run, and Pattern Hit-and-Run for exploration of the [...]

A macromolecular X-ray crystal structure is usually represented as a single static model with a single set of temperature factors representing a simple approximation of motion and disorder of the structure. Multiconformer representations of small proteins have been shown to better describe anisotropic motion and disorder and improve the quality of their electron density maps. Here, we apply multistart simulated annealing crystallographic refinement to a 705 ribosome-RF1 translation termination complex that was recently solved at 3.2 [Angstrom] resolution. The analysis improves the interpretability of the electron density map of this 2.5-MDa ribonucleoprotein complex and provides insights into its structural dynamics. We also used multistart refinement and conventional Fourier difference maps to address a recent study in which cross-crystal averaging between two crystal forms of the 705 ribosome was used to evaluate reported differences between two ribosome crystal structures solved at 2.8 and 3.7 [Angstrom] resolution. Our analysis suggests that results obtained from cross-crystal averaging are inherently biased toward the higher-resolution dataset. multistart refinement | protein synthesis | ribosome dynamics | rRNA doi/10.1073/pnas.0909287106

In this paper, the model parameters of a two-layer dielectric structure with random slightly rough boundaries are retrieved from data that consist of the backscattering coefficients for multiple polarizations, angles, and frequencies. We use the small perturbation method to solve the forward problem. The inversion problem is then formulated as a least square problem and is solved using a global optimization method known as simulated annealing, which is shown to be a robust retrieval algorithm for our purpose. The algorithm performance depends on several parameters. We make recommendations on these parameters and propose a technique for exiting local minima when encountered. We test the sensitivity of the inversion scheme to measurement noise and present the noise analysis results. Index Terms--Global optimization, inverse problem, layered media, random rough surface, simulated annealing, subsurface properties.

This paper proposes a new approach and coding scheme for solving economic dispatch problems in power systems through simulated annealing like particle swarm optimization (SA-PSO). This novel coding scheme could effectively prevent obtaining infeasible solutions through the application of stochastic search methods, thereby dramatically improving search efficiency and solution quality. Many nonlinear characteristics of power generators, and their operational constraints, such as generation limitations, ramp rate limits, prohibited operating zones, transmission loss, and nonlinear cost functions, were all considered for practical operation. The effectiveness and feasibility of the proposed method were demonstrated by four system case studies and compared with previous literature in terms of solution quality and computational efficiency. The experiment showed encouraging results, suggesting that the proposed approach was capable of efficiently determining higher quality solutions addressing economic dispatch problems. Index Terms--Economic dispatch, particle swarm optimization, prohibited zones, ramp rate limits, simulated annealing, valve-point effects.

Decimation is a common technique in statistical physics that is used in the context of Boltzmann machines (BMs) to drastically reduce the computational cost at the learning stage. Decimation allows to analytically evaluate quantifies that should otherwise be statistically estimated by means of Monte Carlo (MC) simulations. However, in its original formulation, this method could only be applied to restricted topologies corresponding to sparsely connected neural networks. In this brief, we present a generalization of the decimation process and prove that it can be used on any BM, regardless of its topology and connectivity. We solve the Monk problem with this algorithm and show that it performs as well as the best classification methods currently available. Index Terms--Boltzmann machines (BMs), decimation, neural networks, simulated annealing (SA).

Nearest neighbor (NN) techniques are commonly used in remote sensing, pattern recognition, and statistics to classify objects into a predefined number of categories based on a given set of predictors. These techniques are particularly useful in those cases exhibiting a highly nonlinear relationship between variables. In most studies, the distance measure is adopted a priori. In contrast, we propose a general procedure to find Euclidean metrics in a low-dimensional space (i.e., one in which the number of dimensions is less than the number of predictor variables) whose main characteristic is to minimize the variance of a given class label of all those pairs of points whose distance is less than a predefined value, k-NN is used in each embedded space to determine the possibility that a query belongs to a given class label. The class estimation is carried out by an ensemble of predictions. To illustrate the application of this technique, a typical land cover classification using a Landsat-5 Thematic Mapper scene is presented. Experimental results indicate substantial improvement with regard to the classification accuracy as compared with approaches such as maximum likelihood, linear discriminant analysis, standard k-NN, and adaptive quasi-conformal kernel k-NN. Index Terms--Dimensionality reduction, ensemble prediction, k-nearest neighbors (NNs), land cover classification, simulated annealing (SA).

This paper presents an integer-linear programming approach for a cell formation problem (CFP) in a dynamic environment with a multi-period planning horizon. The objectives are to minimize the inter-cell movement and machine costs simultaneously. In dynamic environments, the product mix and demand are different but deterministic in each period. As a consequence, the formed cells in the current period may not be optimal for the next period. Thus, the reconfiguration of cells is required. Reconfiguration consists of re-forming part families, machine groups, and machine relocation. The CFP belongs to the category of NP-hard problems, thus we develop an efficient simulated annealing (SA) method to solve such a problem. The proposed mathematical model is optimally solved and the associated results are compared with the results obtained by the SA run. The results show that the gap between optimal and SA solutions is less than 4%, which indicates the efficiency of the developed SA scheme. doi: 10.1057/palgrave.jors.2602436 Keywords: dynamic cell formation problem; mixed-integer programming; simulated annealing

We examine the theoretical and numerical global convergence properties of a certain "gradient free" stochastic approximation algorithm called the "simultaneous perturbation stochastic approximation (SPSA)" that has performed well in complex optimization problems. We establish two theorems on the global convergence of SPSA, the first involving the wellknown method of injected noise. The second theorem establishes conditions under which "basic" SPSA without injected noise can achieve convergence in probability to a global optimum, a result with important practical benefits. Index Terms--Global convergence, simulated annealing, simultaneous perturbation stochastic approximation (SPSA), stochastic approximation (SA), stochastic optimization.

This paper presents a comprehensive review of simulated annealing (SA)-based optimization algorithms. SA-based algorithms solve single and multiobjective optimization problems, where a desired global minimum/maximum is hidden among many local minima/maxima. Three single objective optimization algorithms (SA, SA with tabu search and CSA) and five multiobjective optimization algorithms (SMOSA, UMOSA, PSA, WDMOSA and PDMOSA) based on SA have been presented. The algorithms are briefly discussed and are compared. The key step of SA is probability calculation, which involves building the annealing schedule. Annealing schedule is discussed briefly. Computational results and suggestions to improve the performance of SA-based multiobjective algorithms are presented. Finally, future research in the area of SA is suggested. Keywords: simulated annealing; metaheuristic; multiobjective optimization; annealing schedule

The well-known Taguchi orthogonal arrays, which involve fractional factorials based on orthogonal tables, are incorporated with the classical simulated annealing to enhance the numerical convergence and accuracy of the optimal solution for linear and nonlinear optimization problems. The novel combination of the classical simulated annealing (SA) and fractional factorial analysis is termed the Taguchi-SA. The performance of the proposed Taguchi--SA method is evaluated by computing several representative global optimization problems, such as a multimodal function, nonlinear dynamic control, and large parameter optimization. The numerical results show that the proposed Taguchi-SA method markedly outperforms the classical SA in solving global optimization problems. Additionally, the proposed method is applied to an airfoil shape design and an optimal wing planform design to examine the effectiveness of the Taguchi-SA method in practical aerodynamic optimization design. The pressure distribution results and aerodynamic data, such as lift, drag, and pitching moment coefficients, clearly indicate that the aerodynamic performance of the present airfoil is better than that of the original. The optimal wing planform obtained by the proposed Taguchi-SA method also enhances the aerodynamic performance of the airplane.

Monte Carlo simulations are performed for ion distributions outside a single globular macroin and for a pair of macroions in different salt solutions. Simulations results are compared with the predictions of the Ornstein-Zernike equation with the hypernetted chain closure approximation and the predictions and the nonlinear Poisson-Boltzmann equation, both augmented by pertinent van der Waals terms.

Nucleation and growth during bulk electrodeposition is studied using Kinetic Monte Carlo (KMC) simulations. Ion transport in solution is modeled using Brownian dynamics, and the kinetics of nucleation and growth are dependent on the probabilities of metal-on-substrate and metal-on-metal deposition.an

The characterization and understanding of reaction behavior at interface by modeling three equilibrium-limited chemical reactions at their vapor-liquid (V-L) interfaces is presented. Simple dimerization reactions and more complex multicomponent reactions are modeled using the reactive Monte Carlo (RxMC) simulate technique.

The effects of wafer miscut and water defects on the morphologies of Si (111) surface etched in NH(sub 4)F are studied by implementing a kinetic Monte Carlo (KMC) simulation. The defects result in persistent pit initiation, which leads to the formation of multilevel stacked pits.

A theoretical approach was proposed to investigate the correlation between morphology and breakdown strength. Monte Carlo simulation also confirmed that the breakdown strength changes with the void shape and size distributions and can be improved by orientation.

A density matrix approach is used in combination with a tight-binding model to elucidate electron transport in terahertz quantum cascade lasers and is incorporated into a Monte Carlo simulation. The results show that the inclusion of coherent transport and dephasing in the calculations is essential when transport is dominated by transitions between weakly coupled states.

The scaling characteristics of both n-and p-channel Ge-on-insulator (GOI) as well as silicon-on-insulator (SOI) MOSFETs with channel length ranging from 20-130 nm are studied by a two-dimensional self-consistent fullband Monte Carlo device simulator. The results indicate that both n- and p-channel GOI MOSFETs can be scaled down to the nanoregion, due to the nonstationary transport, especially for the p-channel device.

Self-consistent Monte Carlo simulations are used to study the low noise and high gain potential of InSb avalanche photodiodes. It is found that for an electron-initiated avalanche, excess noise factors well below the minimum McIntyre value persist up to gain values of around 60 for a 3.2 mum avalanche region.

The general model, suitable for different types of defects, is developed to describe the stress-induced oxide traps and the statistical properties of the trap-assisted tunneling current (TAT). The statistical model is validated by means of a Monte Carlo simulator developed to describe the oxide trap generation and the TAT statistics in large Flash memory arrays.

The full-band Monte Carlo simulations of germanium bulk n- and pMOSFETs are performed and compared them with their Si counterparts. The transport in the presence of phonon, ionized impurity, surface roughness scattering, and impact ionization are considered.

Monte Carlo simulations are performed to find out how red blood cell (RBC) clustering increases ultrasound scattering by blood. The two dimensional (under isotropic conditions and anisotropic conditions) results generally predict that low-frequency blood backscatter is related to cluster dimension, and higher frequency properties are affected by finer morphological features as anisotropy.

Gibbs ensemble Monte Carlo simulations of water vapor/liquid equilibrium (VLE) are reported using the flexible fixed charge RWK2 water-water potential. Fixing the bond lengths and HOH angle in the RWK2 interaction at their isolated molecule minimum energy values leads to significant deterioration of the VLE predictions obtained.

New simulated annealing algorithms for the resource-constrained project scheduling problem and its multiple mode version are described. The procedures were tested on public benchmark problems and revealed to be highly efficient.

The purpose of this paper is to investigate the applicability of fuzzy rule-based modeling to classify a LANDSAT TM scene from 1984 of an area located in the south of Germany. Both a land cover map with four different categories and an image depicting the degree of ambiguity of the classification for each pixel is the expected output. The fuzzy classification algorithm will use a rule system derived from a training set using simulated annealing as an optimization algorithm. The results are then validated and compared with a common classification method in order to judge the effectiveness of the proposed technique. It will also be shown that the proposed method with only nine rules for four different land cover classes performs slightly better than the maximum likelihood classifier (MLC). For error assessment, the traditional error matrix and fuzzy operators have been used. Index Terms--Fuzzy classification, land cover, remote sensing, simulated annealing.

A global stability criterion for two constituent parameters of the discrete-time neural network updating in a block-sequential mode is derived, and two deterministic annealing techniques incorporating its stability condition are studied. One technique concerns reducing the decay rate of the membrane potential gradually toward zero; the other relates to increasing the neuron gain gradually toward infinity while updating the neuron states iteratively. It is shown that the deterministic annealing for parallel or partial-parallel updating can be successfully accomplished without falling into sustained oscillations by properly controlling the decay rate of the membrane potential as well as the neuron gain. It is also demonstrated that near-optimal solutions are obtained for parallel, partial-parallel, and sequential updating by the suitable selection of the two constituent parameters. Index Terms - Asynchronous updating, constrained optimization, dynamic systems, stability, synchronous updating.

The implantation-induced disordering of CuPtbeta-ordered quantum GainP and its microstructure are considered. Implantation defects mediate implantation-induced disordering and can be identified using defect annealing modelling techniques. The disordering process is associated with an activation energy of 2.15 eV. Masked implantation of lateral order/disorder structures were established after high resolution electron beam lithography techniques were used to make wire and dot implantation masks of 35 nanometers wide.

A conformational searching algorithm which creates a best-first, depth-first traversal of the conformer graph is discussed. It is never used on the same starting structure more than once and does not, therefore, incur inaccuracy due to diminishing return. The technique is used to plot locally optimal conformations of cycloheptadecane with MM2 energies within 3 kcal per mole of its global optimum. Unlike alternative search techniques, it is useable in potential-energy surface analysis.

The simulated annealing optimization technique has been successfully applied to a number of electrical engineering problems, including transmission system expansion planning. The method is general in the sense that it does not assume any particular property of the problem being solved, such as linearity or convexity. Moreover, it has the ability to provide solutions arbitrarily close to an optimum (i.e. it is asymptotically convergent) as the cooling process slows down. The drawback of the approach is the computational burden: finding optimal solutions may be extremely expensive in some cases. This paper presents a Parallel Simulated Annealing, PSA, algorithm for solving the long term transmission network expansion planning problem. A strategy that does not affect the basic convergence properties of the Sequential Simulated Annealing algorithm have been implemented and tested. The paper investigates the conditions under which the parallel algorithm is most efficient. The parallel implementations have been tested on three example networks: a small 6-bus network, and two complex real-life networks. Excellent results are reported in the test section of the paper: in addition to reductions in computing times, the Parallel Simulated Annealing algorithm proposed in the paper has shown significant improvements in solution quality for the largest of the test networks. Keywords: combinatorial optimization, simulated annealing, network static expansion planning, large scale optimization, parallel computing.

An annealing method is presented for obtaining the optimal design of structural systems. The method uses a random sequence of designs to determine the optimal design. In performing the optimization, the method uses two desirable features: the first is to achieve an automatic reduction of the search range, and the second is to perform sensitivity analysis for the design variables. The present method is not as effective as classical optimal design methods for problems with constraints arising from static loads. However, the method requires less iterations to converge compared to other simulated annealing methods. The present method is advantageous over classical methods for problems with constraints arising from dynamic loading. By contrast, classical optimization methods for problems with constraints arising from dynamic loading either fall to converge or produce designs which may be local minimums. Numerical examples illustrate how the present strategy succeeds while classical optimal design methods fail.

A genetic algorithm (GA) that solves the k-out-of-n:G subsystem redundancy problems for series-parallel systems, and maximizes reliability under the given cost and weight constraints has been developed. The GA is more reliable and effective than previous dynamic, integer, mixed-integer, and nonlinear programming methods. It easily adapts to various diverse design requirements including those with mixing of components and more complex forms of redundancy.

An engineering annealing method for optimal solutions of cellular neural networks is presented. Cellular neural networks are very promising in solving many scientific problems in image processing, pattern recognition, and optimization by the use of stored program with predetermined templates. Hardware annealing, which is a paralleled version of mean-field annealing in analog networks, is a highly efficient method of finding optimal solutions of cellular neural networks. It does not require any stochastic procedure and henceforth can be very fast. The generalized energy function of the network is first increased by reducing the voltage gain of each neuron. Then, the hardware annealing searches for the globally minimum energy state by continuously increasing the gain of neurons. The process of global optimization by the proposed annealing can be described by the eigenvalue problems in the time-varying dynamic system. In typical nonoptimization problems, it also provides enough stimulation to frozen neurons caused by ill-conditioned initial states.

Simulated annealing is a statistical computational technique for obtaining approximate solutions to combinatorial optimization problems. An efficient simulated annealing algorithm is used to design binary sequence sets which have good autocorrelation and crosscorrelation properties. Some of the synthesized results are presented, and their properties are better than any other known in the literature. The synthesized binary sequence sets promise to be practically applicable.

An improved shape annealing algorithm for truss topology generation and optimization, based on the techniques of shape grammars and simulated annealing, is introduced. The algorithm features a shape optimization method using only simulated annealing with a shape grammar move set; while no traditional gradient-based techniques are employed, the algorithm demonstrates more consistent convergence characteristics. By penalizing the objective function for violated constraints, the algorithm incorporates geometric constraints to avoid obstacles. The improved algorithm is illustrated on various structural examples taking into account stress, Euler buckling and geometric constraints, generating a variety of solutions based on a simple grammar.

This paper introduces a simulated annealing-based approach to three-dimensional component packing that employs simulated annealing to generate optimal solutions. Simulated annealing has been used extensively for two-dimensional layout of VLSI circuits; this research extends techniques developed for two-dimensional layout optimization to three-dimensional problems which are more representative of mechanical engineering applications. This research also provides a framework in which to solve general component layout problems.

Quadratic Assignment Problem, one of the classical NP-complete problems, is usually interpreted as a facility layout problem, of which the task is to assign facilities to locations in a manner so as to minimize a total cost function. The complexity of the problem has motivated the development of many approximation procedures. In this paper, we present a new approach to the Quadratic Assignment Problem QAP). The new technique, that is called Guided Evolutionary Simulated Annealing (GESA), is a parallel algorithm. It combines the ideas of simulated evolution and simulated annealing in a novel way. We demonstrate the technique using facility layout problems as examples. Simulation results for the benchmark problems are reported. The results indicate that the GESA method outperforms other approximation methods.

Simulated annealing (SA) is a robust, stable, but computationally costly method for solving ill-posed image-restoration problems. We describe the use of a backprojection operator that identifies those regions of an object estimate that have the greatest likelihood of being in error at each step of the SA process. This reduces computational time by concentrating the computing effort of SA on those pixels most effective in reducing the reconstruction error. The performance of an area-adaptive SA algorithm is evaluated for the restoration of images blurred by a simple pillbox space-invariant and a biconical space-variant point-spread function typical of a depth-measuring optical system.

Simulated annealing has been applied successfully to row-based placement problems. However, its requirement for computation time grows rapidly with the circuit size, making the simulated annealing placement of extra large circuits computationally expensive. We apply ratio cut hierarchical partitioning, which enables efficient multilevel simulated annealing. An overlapping moving window scheme is used to compensate for the effect of partitioning on the placement quality. Through the use of hierarchical partitioning, the asymptotic run time complexity of this algorithm grows linearly as the function of the circuit size. The system is called placement by ratio cut partitioning (PRC). The results from several benchmark tests are presented demonstrating PRC to be 2.49% on average better than Timber-WolfSC Ver5.6. Furthermore, the run time for PRC is 18.3% on average less than that required for TimberWolfSC Ver5.6 for large test cases with more than 2 000 cells. For a 100-K sea of gates test case, a 7.09% reduction in total wire length over TimberWolfSC Ver5.6, and a 51.7% saving in CPU time, were achieved.

A proposed software package produces a graphical representation of an electrical circuit diagram from a PSpice text file and uses simulated annealing for optimization. Applicable to realistic global component connections and circuit design, the algorithm is significant to SPpice users and those who perform program checks by visualizing connections and reviewing component node pairs lists.