Simulated annealing

About: Simulated annealing is a research topic. Over the lifetime, 21436 publications have been published within this topic receiving 563467 citations.

Genetic Algorithms and Simulated Annealing

Abstract: A detergent composition mainly for automatic laundering machines which comprises, on the basis of 100 parts by weight of total composition, at least 60 parts of soap and no more than 10 parts of a mixture of surfactants comprising 10 to 30% of at least one non-ionic polyoxyalkylated surfactant and 90 to 70% of an anionic surfactant selected essentially from alpha -sulfonated fatty acids derivatives, the remainder of the composition comprising at least one ingredient selected from alkaline detergent additives, bleaching agents, optical brighteners, fragrances, antiredeposition agents and enzymes. The non-ionic surfactants are preferably fatty acid amides derived from tallow, copra or palm-oil condensed with polyoxyethylene residues. The anionic surfactants are preferably alpha -sulfonated fatty esters or amides derived from tallow, copra or palm-oil. The proper combination of said non-ionic and anionic surfactants with soaps impart to the laundering compositions an excellent detergent ability and foam control even in very soft waters and non-polluting properties.

Optimization by simulated annealing: an experimental evaluation. Part I, graph partitioning

Abstract: In this and two companion papers, we report on an extended empirical study of the simulated annealing approach to combinatorial optimization proposed by S. Kirkpatrick et al. That study investigated how best to adapt simulated annealing to particular problems and compared its performance to that of more traditional algorithms. This paper (Part I) discusses annealing and our parameterized generic implementation of it, describes how we adapted this generic algorithm to the graph partitioning problem, and reports how well it compared to standard algorithms like the Kernighan-Lin algorithm. (For sparse random graphs, it tended to outperform Kernighan-Lin as the number of vertices become large, even when its much greater running time was taken into account. It did not perform nearly so well, however, on graphs generated with a built-in geometric structure.) We also discuss how we went about optimizing our implementation, and describe the effects of changing the various annealing parameters or varying the basic...

Increasing the precision of comparative models with YASARA NOVA--a self-parameterizing force field.

Abstract: One of the conclusions drawn at the CASP4 meeting in Asilomar was that applying various force fields during refinement of template-based models tends to move predictions in the wrong direction, away from the experimentally determined coordinates. We have derived an all-atom force field aimed at protein and nucleotide optimization in vacuo (NOVA), which has been specifically designed to avoid this problem. NOVA resembles common molecular dynamics force fields but has been automatically parameterized with two major goals: (i) not to make high resolution X-ray structures worse and (ii) to improve homology models built by WHAT IF. Force-field parameters were not required to be physically correct; instead, they were optimized with random Monte Carlo moves in force-field parameter space, each one evaluated by simulated annealing runs of a 50-protein optimization set. Errors inherent to the approximate force-field equation could thus be canceled by errors in force-field parameters. Compared with the optimization set, the force field did equally well on an independent validation set and is shown to move in silico models closer to reality. It can be applied to modeling applications as well as X-ray and NMR structure refinement. A new method to assign force-field parameters based on molecular trees is also presented. A NOVA server is freely accessible at http://www.yasara.com/servers

Search‐based software test data generation: a survey

Abstract: The use of metaheuristic search techniques for the automatic generation of test data has been a burgeoning interest for many researchers in recent years. Previous attempts to automate the test generation process have been limited, having been constrained by the size and complexity of software, and the basic fact that in general, test data generation is an undecidable problem. Metaheuristic search techniques oer much promise in regard to these problems. Metaheuristic search techniques are highlevel frameworks, which utilise heuristics to seek solutions for combinatorial problems at a reasonable computational cost. To date, metaheuristic search techniques have been applied to automate test data generation for structural and functional testing; the testing of grey-box properties, for example safety constraints; and also non-functional properties, such as worst-case execution time. This paper surveys some of the work undertaken in this eld, discussing possible new future directions of research for each of its dieren t individual areas.

Geophysical inversion with a neighbourhood algorithm—I. Searching a parameter space

Abstract: SUMMARY This paper presents a new derivative-free search method for finding models of acceptable data fit in a multidimensional parameter space. It falls into the same class of method as simulated annealing and genetic algorithms, which are commonly used for global optimization problems. The objective here is to find an ensemble of models that preferentially sample the good data-fitting regions of parameter space, rather than seeking a single optimal model. (A related paper deals with the quantitative appraisal of the ensemble.) The new search algorithm makes use of the geometrical constructs known as Voronoi cells to derive the search in parameter space. These are nearest neighbour regions defined under a suitable distance norm. The algorithm is conceptually simple, requires just two ‘tuning parameters’, and makes use of only the rank of a data fit criterion rather than the numerical value. In this way all diYculties associated with the scaling of a data misfit function are avoided, and any combination of data fit criteria can be used. It is also shown how Voronoi cells can be used to enhance any existing direct search algorithm, by intermittently replacing the forward modelling calculations with nearest neighbour calculations. The new direct search algorithm is illustrated with an application to a synthetic problem involving the inversion of receiver functions for crustal seismic structure. This is known to be a non-linear problem, where linearized inversion techniques suVer from a strong dependence on the starting solution. It is shown that the new algorithm produces a sophisticated type of ‘self-adaptive’ search behaviour, which to our knowledge has not been demonstrated in any previous technique of this kind.