Relevant bibliographies by topics / Fast simulated annealing

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  2. Dissertations / Theses
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Academic literature on the topic 'Fast simulated annealing'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Fast simulated annealing"

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Bonilla-Petriciolet, Adrián, Juan Carlos Tapia-Picazo, Carlos Soto-Becerra, and Javier Gerson Zapiain-Salinas. "Perfiles de comportamiento numérico de los métodos estocásticos simulated annealing y very fast simulated annealing en cálculos termodinámicos." Ingeniería, investigación y tecnología 12, no. 1 (January 1, 2011): 51–62. http://dx.doi.org/10.22201/fi.25940732e.2011.12n1.006.

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Szu, Harold, and Ralph Hartley. "Fast simulated annealing." Physics Letters A 122, no. 3-4 (June 1987): 157–62. http://dx.doi.org/10.1016/0375-9601(87)90796-1.

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Henrique Cardoso Camelo, Pedro, and Rafael Lima De Carvalho. "Multilayer Perceptron optimization through Simulated Annealing and Fast Simulated Annealing." Academic Journal on Computing, Engineering and Applied Mathematics 1, no. 2 (June 10, 2020): 28–31. http://dx.doi.org/10.20873/uft.2675-3588.2020.v1n2.p28-31.

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The Multilayer Perceptron (MLP) is a classic and widely used neural network model in machine learning applications. As the majority of classifiers, MLPs need well-defined parameters to produce optimized results. Generally, machine learning engineers use grid search to optimize the hyper-parameters of the models, which requires to re-train the models. In this work, we show a computational experiment using metaheuristics Simulated Annealing and Fast Simulated Annealing for optimization of MLPs in order to optimize the hyper-parameters. In the reported experiment, the model is used to optimize two parameters: the configuration of the neural network layers and its neuron weights. The experiment compares the best MLPs produced by the SA and FastSA using the accuracy and classifier complexity as comparison measures. The MLPs are optimized in order to produce a classifier for the MNIST database. The experiment showed that FastSA has produced a better MLP, using less computational time and less fitness evaluations.
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Camelo, Pedro Henrique Cardoso, and Rafael Lima De Carvalho. "Multilayer Perceptron optimization through Simulated Annealing and Fast Simulated Annealing." Academic Journal on Computing, Engineering and Applied Mathematics 1, no. 2 (June 10, 2020): 28–31. http://dx.doi.org/10.20873/ajceam.v1i2.9474.

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The Multilayer Perceptron (MLP) is a classic and widely used neural network model in machine learning applications. As the majority of classifiers, MLPs need well-defined parameters to produce optimized results. Generally, machine learning engineers use grid search to optimize the hyper-parameters of the models, which requires to re-train the models. In this work, we show a computational experiment using metaheuristics Simulated Annealing and Fast Simulated Annealing for optimization of MLPs in order to optimize the hyper-parameters. In the reported experiment, the model is used to optimize two parameters: the configuration of the neural network layers and its neuron weights. The experiment compares the best MLPs produced by the SA and FastSA using the accuracy and classifier complexity as comparison measures. The MLPs are optimized in order to produce a classifier for the MNIST database. The experiment showed that FastSA has produced a better MLP, using less computational time and less fitness evaluations.
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Ingber, L. "Very fast simulated re-annealing." Mathematical and Computer Modelling 12, no. 8 (1989): 967–73. http://dx.doi.org/10.1016/0895-7177(89)90202-1.

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Lee, Chung-Yeol, Sun-Young Lee, Soo-Min Lee, Jong-Seok Lee, and Cheol-Hoon Park. "Fast Simulated Annealing with Greedy Selection." KIPS Transactions:PartB 14B, no. 7 (December 31, 2007): 541–48. http://dx.doi.org/10.3745/kipstb.2007.14-b.7.541.

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Guo, Hong, Martin Zuckermann, R. Harris, and Martin Grant. "A Fast Algorithm for Simulated Annealing." Physica Scripta T38 (January 1, 1991): 40–44. http://dx.doi.org/10.1088/0031-8949/1991/t38/010.

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Szu, H. H., and R. L. Hartley. "Nonconvex optimization by fast simulated annealing." Proceedings of the IEEE 75, no. 11 (1987): 1538–40. http://dx.doi.org/10.1109/proc.1987.13916.

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Lu, Zhiwu, Yuxin Peng, and Horace H. S. Ip. "Combining multiple clusterings using fast simulated annealing." Pattern Recognition Letters 32, no. 15 (November 2011): 1956–61. http://dx.doi.org/10.1016/j.patrec.2011.09.022.

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Lee, Chang-Yong, and Dongju Lee. "Determination of initial temperature in fast simulated annealing." Computational Optimization and Applications 58, no. 2 (December 24, 2013): 503–22. http://dx.doi.org/10.1007/s10589-013-9631-y.

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Dissertations / Theses on the topic "Fast simulated annealing"

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Savan, Emanuel-Emil. "Consumer liking and sensory attribute prediction for new product development support : applications and enhancements of belief rule-based methodology." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/consumer-liking-and-sensory-attribute-prediction-for-new-product-development-support-applications-and-enhancements-of-belief-rulebased-methodology(0582be52-a5ce-47da-836d-e30b5506fb41).html.

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Methodologies designed to support new product development are receiving increasing interest in recent literature. A significant percentage of new product failure is attributed to a mismatch between designed product features and consumer liking. A variety of methodologies have been proposed and tested for consumer liking or preference prediction, ranging from statistical methodologies e.g. multiple linear regression (MLR) to non-statistical approaches e.g. artificial neural networks (ANN), support vector machines (SVM), and belief rule-based (BRB) systems. BRB has been previously tested for consumer preference prediction and target setting in case studies from the beverages industry. Results have indicated a number of technical and conceptual advantages which BRB holds over the aforementioned alternative approaches. This thesis focuses on presenting further advantages and applications of the BRB methodology for consumer liking prediction. The features and advantages are selected in response to challenges raised by three addressed case studies. The first case study addresses a novel industry for BRB application: the fast moving consumer goods industry, the personal care sector. A series of challenges are tackled. Firstly, stepwise linear regression, principal component analysis and AutoEncoder are tested for predictors’ selection and data reduction. Secondly, an investigation is carried out to analyse the impact of employing complete distributions, instead of averages, for sensory attributes. Moreover, the effect of modelling instrumental measurement error is assessed. The second case study addresses a different product from the personal care sector. A bi-objective prescriptive approach for BRB model structure selection and validation is proposed and tested. Genetic Algorithms and Simulated Annealing are benchmarked against complete enumeration for searching the model structures. A novel criterion based on an adjusted Akaike Information Criterion is designed for identifying the optimal model structure from the Pareto frontier based on two objectives: model complexity and model fit. The third case study introduces yet another novel industry for BRB application: the pastry and confectionary specialties industry. A new prescriptive framework, for rule validation and random training set allocation, is designed and tested. In all case studies, the BRB methodology is compared with the most popular alternative approaches: MLR, ANN, and SVM. The results indicate that BRB outperforms these methodologies both conceptually and in terms of prediction accuracy.
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Betke, Margrit, and Nicholas Makris. "Fast Object Recognition in Noisy Images Using Simulated Annealing." 1995. http://hdl.handle.net/1721.1/7199.

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A fast simulated annealing algorithm is developed for automatic object recognition. The normalized correlation coefficient is used as a measure of the match between a hypothesized object and an image. Templates are generated on-line during the search by transforming model images. Simulated annealing reduces the search time by orders of magnitude with respect to an exhaustive search. The algorithm is applied to the problem of how landmarks, for example, traffic signs, can be recognized by an autonomous vehicle or a navigating robot. The algorithm works well in noisy, real-world images of complicated scenes for model images with high information content.
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Hsieh, Yueh-Hsun, and 謝岳勳. "Very Fast Simulated Annealing and Genetic Algorithm for Pattern Detection and Seismic Applications." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/81283060758832721413.

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碩士
國立交通大學
多媒體工程研究所
98
We adopt four global optimization methods to the parameterized pattern detection. They are simulated annealing (SA), fast simulated annealing (FSA), very fast simulated annealing (VFSA), and genetic algorithm. FSA and VFSA not only avoid local optimum with probability, but also have faster convergence than SA. Genetic algorithm (GA) is also a global optimization algorithm to avoid local minimum. We use these four global optimization methods to design the pattern parameter detection system. Also we propose the sequential methods to detect three types of patterns that include the lines, hyperbolas, circles and ellipses in the image. We use steps in the parameter detection for reducing the computation and getting fast convergence. This system has the capability of searching pattern parameter vectors with global minimal distance between the patterns and the image data. In the experiments, this system with four methods is applied to the image data, one-shot seismic data, the common depth point (CDP) gather data. The system can detect the parameters of direct wave (line) and reflected wave pattern (hyperbola) in the simulated and real one-shot seismograms. The system can detect the hyperbolic patterns in CDP gather data. The detected hyperbolic parameters are used to calculate the root-mean-squared velocity Vrms of the layers. Then we use Vrms to process the normal-moveout (NMO) correction. After stacking, we can get the stacked seismic signals. This system can provide an automatic velocity analysis and can improve the seismic interpretation and further seismic data processing.
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Phan, Son Dang Thai. "Pre-injection reservoir evaluation at Dickman Field, Kansas." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-08-3908.

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I present results from quantitative evaluation of the capability of hosting and trapping CO₂ of a carbonate brine reservoir from Dickman Field, Kansas. The analysis includes estimation of some reservoir parameters such as porosity and permeability of this formation using pre-stack seismic inversion followed by simulating flow of injected CO₂ using a simple injection technique. Liner et at (2009) carried out a feasibility study to seismically monitor CO₂ sequestration at Dickman Field. Their approach is based on examining changes of seismic amplitudes at different production time intervals to show the effects of injected gas within the host formation. They employ Gassmann's fluid substitution model to calculate the required parameters for the seismic amplitude estimation. In contrast, I employ pre-stack seismic inversion to successfully estimate some important reservoir parameters (P- impedance, S- impedance and density), which can be related to the changes in subsurface rocks due to injected gas. These are then used to estimate reservoir porosity using multi-attribute analysis. The estimated porosity falls within a reported range of 8-25%, with an average of 19%. The permeability is obtained from porosity assuming a simple mathematical relationship between porosity and permeability and classifying the rocks into different classes by using Winland R35 rock classification method. I finally perform flow simulation for a simple injection technique that involves direct injection of CO₂ gas into the target formation within a small region of Dickman Field. The simulator takes into account three trapping mechanisms: residual trapping, solubility trapping and mineral trapping. The flow simulation predicts unnoticeable changes in porosity and permeability values of the target formation. The injected gas is predicted to migrate upward quickly, while it migrates slowly in lateral directions. A large amount of gas is concentrated around the injection well bore. Thus my flow simulation results suggest low trapping capability of the original target formation unless a more advanced injection technique is employed. My results suggest further that a formation below our original target reservoir, with high and continuously distributed porosity, is perhaps a better candidate for CO₂ storage.
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Books on the topic "Fast simulated annealing"

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Wells, Brett R. PCB routing using fast simulated annealing. Manchester: University of Manchester, Department of Computer Science, 1995.

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Book chapters on the topic "Fast simulated annealing"

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Łukasik, Szymon, and Piotr Kulczycki. "An Algorithm for Sample and Data Dimensionality Reduction Using Fast Simulated Annealing." In Advanced Data Mining and Applications, 152–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25853-4_12.

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Nguyen, Huy, Simon Viennot, and Kokolo Ikeda. "Fast Optimization of the Pattern Shapes in Board Games with Simulated Annealing." In Advances in Intelligent Systems and Computing, 325–37. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11680-8_26.

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Kulczycki, Piotr, and Szymon Łukasik. "Reduction of Dimension and Size of Data Set by Parallel Fast Simulated Annealing." In Studies in Computational Intelligence, 273–90. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03206-1_19.

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Kuperman, W. A., M. D. Collins, and H. Schmidt. "A Fast Simulated Annealing Algorithm for the Inversion of Marine Sediment Seismo-Acoustic Parameters." In Shear Waves in Marine Sediments, 521–28. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3568-9_60.

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Alzahabi, Ahmed, and Mohamed Y. Soliman. "A Computational Comparison between Optimization Techniques for Well Placement Problem: Mathematical Formulations, Genetic Algorithms, and Very Fast Simulated Annealing." In Optimization of Hydraulic Fracture Stages and Sequencing in Unconventional Formations, 167–84. Boca Raton, FL : Taylor & Francis Group, LLC, [2018] | “CRC Press is an imprint of Taylor & Francis Group, an Informa business.”: CRC Press, 2018. http://dx.doi.org/10.1201/b22269-6.

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Lim, Andrew, and Wenbin Zhu. "A Fast and Effective Insertion Algorithm for Multi-depot Vehicle Routing Problem with Fixed Distribution of Vehicles and a New Simulated Annealing Approach." In Advances in Applied Artificial Intelligence, 282–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11779568_32.

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Shi, Zhiru, W. A. C. Fernando, and A. Kondoz. "Simulated Annealing for Fast Motion Estimation Algorithm in H.264/AVC." In Simulated Annealing - Single and Multiple Objective Problems. InTech, 2012. http://dx.doi.org/10.5772/50974.

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Liñán-García, Ernesto, Helue I. De la Barrera-Gómez, Ana Laura Vázquez-Esquivel, Jesús Aguirre-García, Andrea Isabel Cervantes-Payan, Edgar Osvaldo Escobedo-Hernández, and Luis A. López-Alday. "Solving Vehicle Routing Problem With Multi-Phases Simulated Annealing Algorithm." In Handbook of Research on Emergent Applications of Optimization Algorithms, 508–30. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2990-3.ch022.

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In this chapter, a new multi-phases meta-heuristic algorithm based on Simulated Annealing(SA) is proposed in order to solve the Capacitated Vehicle Routing Problem (CVRP) with stochastic demands. This algorithm is named Multi-Phases Simulated Annealing (MPSA), which has four phases of annealing, which are Fast Quenching Phase (FQP), the Annealing Boltzmann Phase (ABP), the Bose-Einstein Annealing Phase (BEAP), and the Dynamical Equilibrium Phase (DEP). These four phases are applied in different ranges of temperature in the Simulated Annealing. The proposed algorithm is applied to generate very close to optimal solution for a cleaning distribution company. Proposed approach is focused to the Vehicle Routing Problem with homogeneous capacities and stochastic demands to gain solutions where routes are the most economical, so based on this, the proposed algorithm is applied to solve the limited Capacity Vehicle Routing Problem (CVRP), trying to provide more effective and efficient metaheuristics.
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Mukherjee, Soumen, Arunabha Adhikari, and Madhusudan Roy. "Melanoma Identification Using MLP With Parameter Selected by Metaheuristic Algorithms." In Intelligent Innovations in Multimedia Data Engineering and Management, 241–68. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7107-0.ch010.

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Nature-inspired metaheuristic algorithms find near optimum solutions in a fast and efficient manner when used in a complex problem like finding optimum number of neurons in hidden layers of a multi-layer perceptron (MLP). In this chapter, a classification work is discussed of malignant melanoma, which is a type of lethal skin cancer. The classification accuracy is more than 91% with visually imperceptible features using MLP. The results found are comparably better than the related work found in the literature. Finally, the performance of two metaheuristic algorithms (i.e., particle swarm optimization [PSO] and simulated annealing [SA]) are compared and analyzed with different parameters to show their searching nature in the two-dimensional search space of hidden layer neurons.
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Bhattacharjee, Arup Kumar, Soumen Mukherjee, Arindam Mondal, and Dipankar Majumdar. "Metaheuristic-Based Feature Optimization for Portfolio Management." In Metaheuristic Approaches to Portfolio Optimization, 109–25. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8103-1.ch005.

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In the last two to three decades, use of credit cards is increasing rapidly due to fast economic growth in developing countries and worldwide globalization issues. Financial institutions like banks are facing a very tough time due to fast-rising cases of credit card loan payment defaulters. The banking institution is constantly searching for the perfect mechanisms or methods to identify possible defaulters among the whole set of credit card users. In this chapter, the most important features of a credit card holder are identified from a considerably large set of features using metaheuristic algorithms. In this work, a standard data set archived in UCI repository of credit card payments of Taiwan is used. Metaheuristic algorithms like particle swarm optimization, ant colony optimization, and simulated annealing are used to identify the significant sets of features from the given data set. Support vector machine classifier is used to identify the class in this two-class (loan defaulter or not) problem.
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Conference papers on the topic "Fast simulated annealing"

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Szu, Harold. "Fast simulated annealing." In AIP Conference Proceedings Volume 151. AIP, 1986. http://dx.doi.org/10.1063/1.36250.

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Chen, Tung-Chieh, and Yao-Wen Chang. "Modern floorplanning based on fast simulated annealing." In the 2005 international symposium. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1055137.1055161.

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Vakil-Baghmisheh, Mohammad-Taghi, and Alireza Navarbaf. "A modified very fast Simulated Annealing algorithm." In 2008 International Symposium on Telecommunications (IST). IEEE, 2008. http://dx.doi.org/10.1109/istel.2008.4651272.

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Velis, Danilo R., and Tadeusz J. Ulrych. "Traveltime tomography using very fast simulated annealing." In SEG Technical Program Expanded Abstracts 1995. Society of Exploration Geophysicists, 1995. http://dx.doi.org/10.1190/1.1887305.

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Jeong, C. S., and M. H. Kim. "Fast parallel simulated annealing for traveling salesman problem." In 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990. http://dx.doi.org/10.1109/ijcnn.1990.137955.

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Souza, Marcelo Santana de, and Milton J. Porsani. "Weighted AB semblance using very fast simulated annealing." In 15th International Congress of the Brazilian Geophysical Society & EXPOGEF, Rio de Janeiro, Brazil, 31 July-3 August 2017. Brazilian Geophysical Society, 2017. http://dx.doi.org/10.1190/sbgf2017-301.

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Huang, Kou‐Yuan, and Yueh‐Hsun Hsieh. "Seismic pattern detection using very fast simulated annealing." In SEG Technical Program Expanded Abstracts 2011. Society of Exploration Geophysicists, 2011. http://dx.doi.org/10.1190/1.3627414.

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Ruiying, Sun, Yin Xingyao, and Wang Baoli. "Fast stochastic inversion based on simulated annealing algorithm." In Beijing 2014 International Geophysical Conference & Exposition, Beijing, China, 21-24 April 2014. Society of Exploration Geophysicists and Chinese Petroleum Society, 2014. http://dx.doi.org/10.1190/igcbeijing2014-140.

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Bashath, Samar, and Amelia Ritahani Ismail. "Improved Particle Swarm Optimization By Fast Simulated Annealing Algorithm." In 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT). IEEE, 2019. http://dx.doi.org/10.1109/icaiit.2019.8834515.

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Carmo, L. M. K., G. Garabito, and e. Costa. "Otimização Global: Simulated Annealing vs. Very Fast Simulated Annealing aplicados no problema de otimização do método SRC 2D." In 9th International Congress of the Brazilian Geophysical Society. European Association of Geoscientists & Engineers, 2005. http://dx.doi.org/10.3997/2214-4609-pdb.160.sbgf304.

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