Placement over containers with fixed dimensions solved with adaptive neighborhood simulated annealing

Martins, Thiago C.; Tsuzuki, Marcos de Sales Guerra

doi:10.2478/v10175-010-0129-9

Cited by 16 publications

(3 citation statements)

References 18 publications

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“…The FEM problems were solved using CG method with Incomplete Choleski decomposition preconditioning [7]. The neighborhood heuristic used by the SA was taken from [10,14], changing only a single conductivity parameter at each iteration and reducing the modifications on parameters that lead to rejected solutions. The divergence probability P err was arbitrarily defined as 1/100.…”

Section: Resultsmentioning

confidence: 99%

Comparison of Different Techniques for Electrical Impedance Tomography Reconstruction using Simulated Annealing and GPU Parallelization

Martins

Tavares

Kian

et al. 2013

Biomedical Engineering

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Electrical Impedance Tomography (EIT) is an imaging technique that attempts to reconstruct the conductivity distribution inside an object from electrical currents and potentials applied and measured at its surface. The EIT reconstruction problem is approached as an optimization problem. This optimization problem can be solved using Simulated Annealing (SA), but at a high computational cost. To reduce the computational load, it is possible to use an incomplete evaluation of the objective function. Two objective functions are analyzed and compared: Euclidian distance and least square minimization. The Euclidian distance minimization showed to present an outside-in behavior, determining the impedance of the external elements first, similar to a layer striping algorithm. It also presents the impact of using GPU for parallelizing matrixvector multiplication. Results with experimental data are presented.

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Section: Resultsmentioning

confidence: 99%

Comparison of Different Techniques for Electrical Impedance Tomography Reconstruction using Simulated Annealing and GPU Parallelization

Martins

Tavares

Kian

et al. 2013

Biomedical Engineering

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“…The control variable is called the crystallization factor, and it represents the standard deviation of the probability distribution. Figure 1 shows the two phases of SA: exploration and refinement [19]. Associated with the phase, the probability distribution standard deviation is also represented.…”

Section: How To Modify the Solution With Continuous Variablesmentioning

confidence: 99%

Using Feedback Strategies in Simulated Annealing with Crystallization Heuristic and Applications

et al. 2021

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This paper represents how typical advanced engineering design can be structured using a set of parameters and objective functions corresponding to the nature of the problem. The set of parameters can be in different types, including integer, real, cyclic, combinatorial, interval, etc. Similarly, the objective function can be presented in various types including integer (discrete), float, and interval. The simulated annealing with crystallization heuristic can deal with all these combinations of parameters and objective functions when the crystallization heuristic presents a sensibility for real parameters. Herein, simulated annealing with the crystallization heuristic is enhanced by combining Bates and Gaussian distributions and by incorporating feedback strategies to emphasize exploration or refinement, or a combination of the two. The problems that are studied include solving an electrical impedance tomography problem with float parameters and a partially evaluated objective function represented by an interval requiring the solution of 32 sparse linear systems defined by the finite element method, as well as an airplane design problem with several parameters and constraints used to reduce the explored domain. The combination of the proposed feedback strategies and simulated annealing with the crystallization heuristic is compared with existing simulated annealing algorithms and their benchmark results are shown. The enhanced simulated annealing approach proposed herein showed better results for the majority of the studied cases.

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“…To solve this type of problem, Martins & Tsuzuki [10] proposed a simulated quenching with a new heuristic to determine the next candidate that managed to solve this type of problem.…”

Section: Integer Objective Function With Float Parametersmentioning

confidence: 99%

Adaptive Neighborhood Heuristics for Simulated Annealing over Continuous Variables

Martins¹,

Sato²,

Tsuzuki³

2012

Simulated Annealing - Advances, Applications and Hybridizations

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Placement over containers with fixed dimensions solved with adaptive neighborhood simulated annealing

Cited by 16 publications

References 18 publications

Comparison of Different Techniques for Electrical Impedance Tomography Reconstruction using Simulated Annealing and GPU Parallelization

Comparison of Different Techniques for Electrical Impedance Tomography Reconstruction using Simulated Annealing and GPU Parallelization

Using Feedback Strategies in Simulated Annealing with Crystallization Heuristic and Applications

Adaptive Neighborhood Heuristics for Simulated Annealing over Continuous Variables

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