Relevant bibliographies by topics / Simulated Annealing Optimization

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Academic literature on the topic 'Simulated Annealing Optimization'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Simulated Annealing Optimization"

1

Brooks, S. P., and B. J. T. Morgan. "Optimization Using Simulated Annealing." Statistician 44, no. 2 (1995): 241. http://dx.doi.org/10.2307/2348448.

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Hamma, Beidi, Sami Viitanen, and Aimo Törn. "Parallel continuous simulated annealing for global optimization simulated annealing∗." Optimization Methods and Software 13, no. 2 (January 2000): 95–116. http://dx.doi.org/10.1080/10556780008805777.

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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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Ioannidis, Yannis E., and Eugene Wong. "Query optimization by simulated annealing." ACM SIGMOD Record 16, no. 3 (December 1987): 9–22. http://dx.doi.org/10.1145/38714.38722.

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Kalai, Adam Tauman, and Santosh Vempala. "Simulated Annealing for Convex Optimization." Mathematics of Operations Research 31, no. 2 (May 2006): 253–66. http://dx.doi.org/10.1287/moor.1060.0194.

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Faber, Richard, Tobias Jockenhövel, and George Tsatsaronis. "Dynamic optimization with simulated annealing." Computers & Chemical Engineering 29, no. 2 (January 2005): 273–90. http://dx.doi.org/10.1016/j.compchemeng.2004.08.020.

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Haddock, Jorge, and John Mittenthal. "Simulation optimization using simulated annealing." Computers & Industrial Engineering 22, no. 4 (October 1992): 387–95. http://dx.doi.org/10.1016/0360-8352(92)90014-b.

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Bertsimas, Dimitris, and Omid Nohadani. "Robust optimization with simulated annealing." Journal of Global Optimization 48, no. 2 (December 3, 2009): 323–34. http://dx.doi.org/10.1007/s10898-009-9496-x.

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Donnelly, Robert A. "Geometry optimization by simulated annealing." Chemical Physics Letters 136, no. 3-4 (May 1987): 274–78. http://dx.doi.org/10.1016/0009-2614(87)80250-6.

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Dissertations / Theses on the topic "Simulated Annealing Optimization"

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Sakhavat, Tamim, Haithem Grissa, and Ziyad Abdalrahman. "Simulated Annealing : Simulated Annealing for Large Scale Optimization in Wireless Communications." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-24606.

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In this thesis a simulated annealing algorithm is employed as an optimization tool for a large scale optimization problem in wireless communication. In this application, we have 100 places for transition antennas and 100 places for receivers, and also a channel between each position in both areas. Our aim is to nd, say the best 3 positions there, in a way that the channel capacity is maximized. The number of possible combinations is huge. Hence, nding the best channel will take a very long time using an exhaustive search. To solve this problem, we use a simulated annealing algorithm and estimate the best answer. The simulated annealing algorithm chooses a random element, and then from the local search algorithm, compares the selected element with its neighbourhood. If the selected element is the maximum among its neighbours, it is a local maximum. The strength of the simulated annealing algorithm is its ability to escape from local maximum by using a random mechanism that mimics the Boltzmann statistic.
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Allred, Kory J. "Horizontal alignment optimization using simulated annealing /." Available to subscribers only, 2006. http://proquest.umi.com/pqdweb?did=1240703751&sid=3&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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Nunes, Luís. "Monitoring networks optimization with simulated annealing." Doctoral thesis, Instituto Superior Técnico - Universidade Técnica de Lisboa, 2003. http://hdl.handle.net/10400.1/1160.

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Tese dout., Ciências de Engenharia, Instituto Superior Técnico, Universidade Técnica de Lisboa, 2003
In this work some methods to optimize environmental monitoring networks are proposed. These methods share simulated annealing as the approximation algorithm. Only monitoring networks reduction is treated here. Monitoring network optimization is a very actual problem given the large number of existing networks in many countries operating large numbers of stations, some of which may be redundant, with very high exploitation costs. Difficulties appear when exploitation costs pushes the dimension of a network towards a minimum, and the statistical reliability pushes in the opposite direction. Finding the optimal dimension may be a very difficult optimization problem due to the large number of combinations, even for small network dimensions. Further complications appear when the available data is too incomplete or come from different homogeneous areas. Some practical answers to these problems were sought in this work. Results showed that optimizing a monitoring network dimension and location of stations, without compromising the quality of the collected data, could attain large reductions in exploitation costs. Simulated annealing showed to be a very flexible and efficient algorithm.
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Gu, Xiaoqing. "The behavior of simulated annealing in stochastic optimization." [Ames, Iowa : Iowa State University], 2008.

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Osman, Ibrahim Hassan. "Metastrategy : simulated annealing and tabu search for combinatorial optimization problems." Thesis, Imperial College London, 1991. http://hdl.handle.net/10044/1/7596.

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Leite, Joao Paulo de Barros. "Parallel adaptive search techniques for structural optimization." Thesis, Heriot-Watt University, 1996. http://hdl.handle.net/10399/1242.

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Moins, Stephane. "Implementation of a Simulated Annealing algorithm for Matlab." Thesis, Linköping University, Department of Electrical Engineering, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1344.

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In this report we describe an adaptive simulated annealing method for sizing the devices in analog circuits. The motivation for use an adaptive simulated annealing method for analog circuit design are to increase the efficiency of the design circuit. To demonstrate the functionality and the performance of the approach, an operational transconductance amplifier is simulated. The circuit is modeled with symbolic equations that are derived automatically by a simulator.

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Rogers, Timothy James. "Pwr fuel assembly optimization using adaptive simulated annealing coupled with translat." [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-3024.

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Barnes, Robert Otto II. "Concurrency Optimization for Integrative Network Analysis." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/23220.

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Virginia Tech\'s Computational Bioinformatics and Bio-imaging Laboratory (CBIL) is exploring integrative network analysis techniques to identify subnetworks or genetic pathways that contribute to various cancers. Chen et. al. developed a bagging Markov random field (BMRF)-based approach which examines gene expression data with prior biological information to reliably identify significant genes and proteins. Using random resampling with replacement (bootstrapping or bagging) is essential to confident results but is computationally demanding as multiple iterations of the network identification (by simulated annealing) is required. The MATLAB implementation is computationally demanding, employs limited concurrency, and thus time prohibitive. Using strong software development discipline we optimize BMRF using algorithmic, compiler, and concurrency techniques (including Nvidia GPUs) to alleviate the wall clock time needed for analysis of large-scale genomic data. Particularly, we decompose the BMRF algorithm into functional blocks, implement the algorithm in C/C++ and further explore the C/C++ implementation with concurrency optimization. Experiments are conducted with simulation and real data to demonstrate that a significant speedup of BMRF can be achieved by exploiting concurrency opportunities. We believe that the experience gained by this research shall help pave the way for us to develop computationally efficient algorithms leveraging concurrency, enabling researchers to efficiently analyze larger-scale data sets essential for furthering cancer research.
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Yip, Pui-Chiu. "The role of regional guidance in optimization: The guided evolutionary simulated annealing approach." Case Western Reserve University School of Graduate Studies / OhioLINK, 1993. http://rave.ohiolink.edu/etdc/view?acc_num=case1056998256.

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Books on the topic "Simulated Annealing Optimization"

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Ilow, Jacek. Simulated annealing optimization in blind equalization. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1993.

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Ginneken, L. P. P. P. van., ed. The annealing algorithm. Boston: Kluwer Academic Publishers, 1989.

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P. J. M. van Laarhoven. Theoretical and computational aspects of simulated annealing. [Amsterdam, Netherlands]: Centrum voor Wiskunde en Informatica, 1988.

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Junior, Hime Aguiar e. Oliveira. Stochastic global optimization and its applications with fuzzy adaptive simulated annealing. Heidelberg: New York, 2012.

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Aguiar e Oliveira Junior, Hime, Lester Ingber, Antonio Petraglia, Mariane Rembold Petraglia, and Maria Augusta Soares Machado. Stochastic Global Optimization and Its Applications with Fuzzy Adaptive Simulated Annealing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27479-4.

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Quantum quenching, annealing and computation. Heidelberg: Springer, 2010.

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Jan, Korst, ed. Simulated annealing and Boltzmann machines: A stochastic approach to combinatorial optimization and neural computing. Chichester [England]: Wiley, 1989.

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8

Valqui, Vidal René Victor, ed. Applied simulated annealing. Berlin: Springer-Verlag, 1993.

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1956-, Kalivas John H., ed. Adaption of simulated annealing to chemical optimization problems. Amsterdam: Elsevier, 1995.

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Chakrabarti, Bikas K., and Arnab Das. Quantum Annealing and Related Optimization Methods. Springer, 2010.

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Book chapters on the topic "Simulated Annealing Optimization"

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Du, Ke-Lin, and M. N. S. Swamy. "Simulated Annealing." In Search and Optimization by Metaheuristics, 29–36. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41192-7_2.

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Bangert, Patrick. "Optimization: Simulated Annealing." In Optimization for Industrial Problems, 165–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24974-7_7.

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El Afia, Abdellatif, Mohamed Lalaoui, and El-ghazali Talbi. "Quaternion Simulated Annealing." In Heuristics for Optimization and Learning, 299–314. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58930-1_20.

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Ansari, Nirwan, and Edwin Hou. "Simulated Annealing and Stochastic Machines." In Computational Intelligence for Optimization, 47–69. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6331-0_4.

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Koakutsu, Seiichi, and Hironori Hirata. "Genetic simulated annealing for floorplan design." In System Modelling and Optimization, 268–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/bfb0035475.

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Robini, Marc C. "Theoretically Grounded Acceleration Techniques for Simulated Annealing." In Handbook of Optimization, 311–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30504-7_13.

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Lee, Jong-Seok, Cheol Hoon Park, and Touradj Ebrahimi. "Theory and Applications of Hybrid Simulated Annealing." In Handbook of Optimization, 395–422. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30504-7_16.

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Mockus, Jonas, William Eddy, Audris Mockus, Linas Mockus, and Gintaras Reklaitis. "Batch Process Scheduling Using Simulated Annealing." In Nonconvex Optimization and Its Applications, 245–59. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-2627-5_15.

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Pourbaix, Dimitri. "Orbit determination of binary stars using simulated annealing." In Applied Optimization, 337–47. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4613-3279-4_21.

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Wee, Chee Keong, and Richi Nayak. "Data Replication Optimization Using Simulated Annealing." In Communications in Computer and Information Science, 222–34. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1699-3_18.

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Conference papers on the topic "Simulated Annealing Optimization"

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Ioannidis, Yannis E., and Eugene Wong. "Query optimization by simulated annealing." In the 1987 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 1987. http://dx.doi.org/10.1145/38713.38722.

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Nahar, S., S. Sahni, and E. Shragowitz. "Simulated Annealing and Combinatorial Optimization." In 23rd ACM/IEEE Design Automation Conference. IEEE, 1986. http://dx.doi.org/10.1109/dac.1986.1586103.

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Nahar, Surendra, Sartaj Sahni, and Eugene Shragowitz. "Simulated annealing and combinatorial optimization." In the 23rd ACM/IEEE conference. New York, New York, USA: ACM Press, 1986. http://dx.doi.org/10.1145/318013.318059.

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Luo, Ya-Zhong, and Guo-Jin Tang. "Parallel Simulated Annealing Using Simplex Method." In 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-4584.

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Besnard, Eric, Nicolas Cordier-Lallouet, Orhan Kural, and Hsin Chen. "Design/optimization with advanced simulated annealing." In 37th Aerospace Sciences Meeting and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1999. http://dx.doi.org/10.2514/6.1999-186.

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Gelfand, Saul, and Sanjoy Mitter. "Analysis of simulated annealing for optimization." In 1985 24th IEEE Conference on Decision and Control. IEEE, 1985. http://dx.doi.org/10.1109/cdc.1985.268603.

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Zhou, Huan, Haitao Li, and Guangliang Dong. "UV Coverage Optimization with Simulated Annealing." In 2014 IEEE International Conference on Computer and Information Technology (CIT). IEEE, 2014. http://dx.doi.org/10.1109/cit.2014.24.

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Goh, Terence K. L. "Simulated annealing optimization in chamfer matching." In Photonics East '96, edited by David P. Casasent. SPIE, 1996. http://dx.doi.org/10.1117/12.256316.

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König, M., and U. Beißert. "Construction Scheduling Optimization by Simulated Annealing." In 26th International Symposium on Automation and Robotics in Construction. International Association for Automation and Robotics in Construction (IAARC), 2009. http://dx.doi.org/10.22260/isarc2009/0027.

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Desouky, Tamer. "OPC recipe optimization using simulated annealing." In SPIE Photomask Technology, edited by M. Warren Montgomery and Wilhelm Maurer. SPIE, 2010. http://dx.doi.org/10.1117/12.864214.

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Reports on the topic "Simulated Annealing Optimization"

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Gelfand, Saul B., and Sanjoy K. Mitter. Analysis of Simulated Annealing for Optimization. Fort Belvoir, VA: Defense Technical Information Center, September 1985. http://dx.doi.org/10.21236/ada170174.

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Gelfand, Saul B., and Sanjoy K. Mitter. Simulated Annealing Type Algorithms for Multivariate Optimization. Fort Belvoir, VA: Defense Technical Information Center, January 1989. http://dx.doi.org/10.21236/ada460156.

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Sun, Xuejun, and Wei Qian. Optimization of CAD System Using Adaptive Simulated Annealing for Digital Mammography. Fort Belvoir, VA: Defense Technical Information Center, July 2003. http://dx.doi.org/10.21236/ada418349.

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Kumar, P. R., and Vasant B. Rao. Asymptotics in Time, Temperature and Size for Optimization by Simulated Annealing: Theory, Practice and Applications. Fort Belvoir, VA: Defense Technical Information Center, January 1990. http://dx.doi.org/10.21236/ada217680.

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