A Computational Comparison between Optimization Techniques for Well Placement Problem: Mathematical Formulations, Genetic Algorithms, and Very Fast Simulated Annealing

The image registration is a very important task in image processing. In the field of medical imaging, it is used to compare the anatomical structures of two or more images taken at different time to track for example the evolution of a disease. Intensity-based techniques are widely used in the multi-modal registration. To have the best registration, a cost function expressing the similarity between these images is maximized. The registration problem is reduced to the optimization of a cost function. We propose to use neighborhood meta-heuristics (tabu search, simulated annealing) and a meta-heuristic population (genetic algorithms). An evaluation step is necessary to estimate the quality of registration obtained. In this paper we present some results of medical image registration

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Optimizing thin‐layer mapping through Spectral Inversion: Performance of Genetic Algorithms and Simulated Annealing

10.1190/1.3513149 ◽

2010 ◽

Cited By ~ 1

Author(s):

Kefyn Paola Castaño ◽

Germán Ojeda

Keyword(s):

Genetic Algorithms ◽

Simulated Annealing ◽

Thin Layer ◽

Spectral Inversion

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Genetic algorithms and simulated annealing for gene mapping

Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence ◽

10.1109/icec.1994.349920 ◽

2002 ◽

Cited By ~ 6

Author(s):

J. Gunnels ◽

P. Cull ◽

J.L. Holloway

Keyword(s):

Genetic Algorithms ◽

Simulated Annealing ◽

Gene Mapping

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Optimizing machine learning models for granular NdFeB magnets by very fast simulated annealing

Scientific Reports ◽

10.1038/s41598-021-83315-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hyeon-Kyu Park ◽

Jae-Hyeok Lee ◽

Jehyun Lee ◽

Sang-Koog Kim

Keyword(s):

Machine Learning ◽

Simulated Annealing ◽

Permanent Magnets ◽

Supervised Machine Learning ◽

Support Vector ◽

Micromagnetic Simulations ◽

Ndfeb Magnets ◽

Average Grain Size ◽

Macroscopic Properties ◽

Very Fast Simulated Annealing

AbstractThe macroscopic properties of permanent magnets and the resultant performance required for real implementations are determined by the magnets’ microscopic features. However, earlier micromagnetic simulations and experimental studies required relatively a lot of work to gain any complete and comprehensive understanding of the relationships between magnets’ macroscopic properties and their microstructures. Here, by means of supervised learning, we predict reliable values of coercivity (μ0Hc) and maximum magnetic energy product (BHmax) of granular NdFeB magnets according to their microstructural attributes (e.g. inter-grain decoupling, average grain size, and misalignment of easy axes) based on numerical datasets obtained from micromagnetic simulations. We conducted several tests of a variety of supervised machine learning (ML) models including kernel ridge regression (KRR), support vector regression (SVR), and artificial neural network (ANN) regression. The hyper-parameters of these models were optimized by a very fast simulated annealing (VFSA) algorithm with an adaptive cooling schedule. In our datasets of randomly generated 1,000 polycrystalline NdFeB cuboids with different microstructural attributes, all of the models yielded similar results in predicting both μ0Hc and BHmax. Furthermore, some outliers, which deteriorated the normality of residuals in the prediction of BHmax, were detected and further analyzed. Based on all of our results, we can conclude that our ML approach combined with micromagnetic simulations provides a robust framework for optimal design of microstructures for high-performance NdFeB magnets.

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