Traditional Simulated Annealing Research Articles

Applying evolutionary programming to structural optimization of atomic clusters

Evolutionary programming is used to study the structural optimization of atomic clusters. We find that this algorithm can successfully find the lowest energy structure of clusters efficiently without relying on any traditional local minimization methods or scheduled simulated annealing methods.

Automatic Histogram and Variogram Reproduction in Simulated Annealing Simulation

Simulated annealing simulation (SAS) is a flexible tool for generating stochastic simulations conditioned to data at various scales and precision. However, a number of important drawbacks exists: (1) SAS requires considerable CPU time, (2) it requires experience in setting the so-called cooling schedule to obtain convergence, and (3) its space of uncertainty is not well understood. In this paper, I propose a new simulated annealing method that guarantees histogram and variogram reproduction through the implementation of a novel perturbation mechanism. The novel perturbation method is based on the Metropolis–Hastings sampler for a Markov-type random field. This new annealing method allows removal of the histogram and variogram from the objective function in the simulated annealing procedure. Furthermore, for the proposed method one finds that (1) the uncertainty space can be quantified, (2) the convergence properties can be linked to convergence of stationary Markov chains, and (3) the convergence speed is improved over traditional simulated annealing.

Application of Temperature Parallel Simulated Annealing to Loading Pattern Optimizations of Pressurized Water Reactors

Temperature parallel simulated annealing (TPSA) was applied to in-core fuel management optimizations, and the optimization performance was evaluated by comparing TPSA with traditional simulated annealing (SA). The TPSA method is an optimization algorithm that is based on SA, but has several distinguishing features: an automatic temperature annealing schedule, time homogeneity, and a significant affinity with parallel execution. The calculation results of a test problem revealed that TPSA was superior to traditional SA in terms of detailed loading pattern optimizations. The reason for this is that the TPSA temperature annealing schedule can effectively avoid local optima by repeating a cooling and heating cycle automatically.

Comparing search strategies for finding global optima on energy landscapes

We provide some tests of the convex global underestimator (CGU) algorithm, which aims to find global minima on funnel-shaped energy landscapes. We use two different potential functions—the reduced Lennard–Jones cluster potential, and the modified Sun protein folding potential, to compare the CGU algorithm with the simplest versions of the traditional trajectory-based search methods, simulated annealing (SA), and Monte Carlo (MC). For both potentials, the CGU reaches energies lower on the landscapes than both SA and MC, even when SA and MC are given the same number of starting points as in a full CGU run or when all methods are given the same amount of computer time. The CGU consistently finds the global minima of the Lennard–Jones potential for all cases with up to at least n=30 degrees of freedom. Finding the global or near-global minimum in the CGU method requires polynomial time [scaling between O(n3) and O(n4)], on average. ©1999 John Wiley & Sons, Inc. J Comput Chem 20: 1527–1532, 1999

Quantum annealing of a disordered magnet

Traditional simulated annealing uses thermal fluctuations for convergence in optimization problems. Quantum tunneling provides a different mechanism for moving between states, with the potential for reduced time scales. Thermal and quantum annealing are compared in a model disordered magnet, where the effects of quantum mechanics can be tuned by varying an applied magnetic field. The results indicate that quantum annealing hastens convergence to the optimum state.

Synthesis of utility systems with variable demands using simulated annealing

A combinatorial optimization approach is proposed to design systems that satisfy time-varying utility demands. A numerical example illustrates the application of the procedure and is used to compare the traditional simulated annealing algorithm with a nonequilibrium version.

Nonequilibrium simulated-annealing algorithm applied to reliability optimization of complex systems

This paper applies an improved nonequilibrium simulated-annealing (I-NESA) technique to find: (1) the global optimum of system cost of two kinds of complex systems subject to constraints on system reliability, and (2) the optimum number of redundancies which maximize the system reliability, subject to constraints on system cost, weight, and volume in a multistage mixed system. The efficacy of I-NESA in solving both varieties of problems is demonstrated by comparing its results with those of simulated annealing (SA). I-NESA, using the Glauber algorithm and an exponential cooling schedule, provides a stable global solution to all the problems considered. The essential features of I-NESA, (1) the nonequilibrium concept while coming out of an inner iteration, and (2) incorporation of the simplex-like heuristic, make it very fast and stable in obtaining the global solution when compared to the traditional SA. Fast convergence was observed in all the problems studied. I-NESA is a useful alternative to either indirect optimization methods or to some random search techniques, in solving problems like those in this paper.

Labeling of MR brain images using Boolean neural network

Presents a knowledge-based approach for labeling two-dimensional (2-D) magnetic resonance (MR) brain images using the Boolean neural network (BNN), which has binary inputs and outputs, integer weights, fast learning and classification, and guaranteed convergence. The approach consists of two components: a BNN clustering algorithm and a constraint satisfying Boolean neural network (CSBNN) labeling procedure. The BNN clustering algorithm is developed to initially segment an image into a number of regions. Then the segmented regions are labeled with the CSBNN, which is a modified version of BNN. The CSBNN uses a knowledge base that contains information on image-feature space and tissue models as constraints. The method is tested using sets of MR brain images. The regions of the different brain tissues are satisfactorily segmented and labeled. A comparison with the Hopfield neural network and the traditional simulated annealing method for image labeling is provided. The comparison results show that the CSBNN approach offers a fast, feasible, and reliable alternative to the existing techniques for medical image labeling.