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Academic literature on the topic 'The Simulated Annealing Algorithm'

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Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "The Simulated Annealing Algorithm"

Lin, Ying Jian, and Xiao Ji Chen. "Simulated Annealing Algorithm Improved BP Learning Algorithm." Applied Mechanics and Materials 513-517 (February 2014): 734–37. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.734.

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BP learning algorithm has advantage of simple structure, easy to implement and so on, it has gained wide application in the malfunction diagnosis and pattern recognition etc.. For BP algorithm is easy to fall into local minima shortcoming cites simulated annealing algorithm. Firstly, study the basic idea of BP learning algorithm and its simple mathematical representation; Then, research simulated annealing algorithm theory and annealing processes; Finally, the study makes BP algorithm combine with simulated annealing algorithm to form a hybrid optimization algorithm of simulated annealing algorithm based on genetic and improved BP algorithm, and gives specific calculation steps. The results show that the content of this study give full play to their respective advantages of two algorithms, make best use of the advantages and bypass the disadvantages, whether in academic or in the application it has a very important significance.

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Du, Gai Li, and Nian Xue. "The Research on Mutil-Objective Location Routing Problem Based on Genetic Simulated Annealing Algorithm." Applied Mechanics and Materials 543-547 (March 2014): 2842–45. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2842.

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This paper analysis the basic principles of the genetic algorithm (GA) and simulated annealing algorithm (SA) thoroughly. According to the characteristics of mutil-objective location routing problem, the paper designs the hybrid genetic algorithm in various components, and simulate achieved the GSAA (Genetic Simulated Annealing Algorithm).Which architecture makes it possible to search the solution space easily and effectively without overpass computation. It avoids effectively the defects of premature convergence in traditional genetic algorithm, and enhances the algorithms global convergence. Also it improves the algorithms convergence rate to some extent by using the accelerating fitness function. Still, after comparing with GA and SA, the results show that the proposed Genetic Simulated Annealing Algorithm has better search ability. And the emulation experiments show that this method is valid and practicable.

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Gong, Guanglu, Yong Liu, and Minping Qian. "An adaptive simulated annealing algorithm." Stochastic Processes and their Applications 94, no. 1 (July 2001): 95–103. http://dx.doi.org/10.1016/s0304-4149(01)00082-5.

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Whitaker, D. "A nested simulated annealing algorithm." Journal of Statistical Computation and Simulation 53, no. 3-4 (December 1995): 233–41. http://dx.doi.org/10.1080/00949659508811708.

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Yao, Xin. "A new simulated annealing algorithm." International Journal of Computer Mathematics 56, no. 3-4 (January 1995): 161–68. http://dx.doi.org/10.1080/00207169508804397.

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Delamarre, D., and B. Virot. "Simulated annealing algorithm : technical improvements." RAIRO - Operations Research 32, no. 1 (1998): 43–73. http://dx.doi.org/10.1051/ro/1998320100431.

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Boissin, Nicolas, and Jean-Luc Lutton. "A parallel simulated annealing algorithm." Parallel Computing 19, no. 8 (August 1993): 859–72. http://dx.doi.org/10.1016/0167-8191(93)90070-2.

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Shao, Wei, and Guangbao Guo. "Multiple-Try Simulated Annealing Algorithm for Global Optimization." Mathematical Problems in Engineering 2018 (July 17, 2018): 1–11. http://dx.doi.org/10.1155/2018/9248318.

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Simulated annealing is a widely used algorithm for the computation of global optimization problems in computational chemistry and industrial engineering. However, global optimum values cannot always be reached by simulated annealing without a logarithmic cooling schedule. In this study, we propose a new stochastic optimization algorithm, i.e., simulated annealing based on the multiple-try Metropolis method, which combines simulated annealing and the multiple-try Metropolis algorithm. The proposed algorithm functions with a rapidly decreasing schedule, while guaranteeing global optimum values. Simulated and real data experiments including a mixture normal model and nonlinear Bayesian model indicate that the proposed algorithm can significantly outperform other approximated algorithms, including simulated annealing and the quasi-Newton method.

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Liang, Rui Hua, Xin Peng Du, Qing Bo Zhao, and Li Zhi Cheng. "Sparse Signal Recovery Based on Simulated Annealing." Applied Mechanics and Materials 321-324 (June 2013): 1295–98. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1295.

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Sparse signal recovery is a hot topic in the fields of optimization theory and signal processing. Two main algorithmic approaches, i.e. greedy pursuit algorithms and convex relaxation algorithms have been extensively used to solve this problem. However, these algorithms cannot guarantee to find the global optimum solution, and then they perform poorly when the sparsity level is relatively large. Based on the simulated annealing algorithm and greedy pursuit algorithms, we propose a novel algorithm on solving the sparse recovery problem. Numerical simulations show that the proposed algorithm has very good recovery performance.

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Li, Xiaocong, Zhanying Wang, Junhua Xu, and Baochao Chen. "Power System Stabilizer Parameters Designing Based on Genetic Simulated Annealing Algorithm." Journal of Clean Energy Technologies 4, no. 3 (2015): 178–82. http://dx.doi.org/10.7763/jocet.2016.v4.275.

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Dissertations / Theses on the topic "The Simulated Annealing Algorithm"

Seacat, Russell Holland III. "Parallelization of the simulated annealing algorithm." Diss., The University of Arizona, 1993. http://hdl.handle.net/10150/186551.

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Nuclear medicine imaging involves the introduction of a radiopharmaceutical into the body and the subsequent detection of the radiation emanating from the organ at which the procedure was directed. The data set resulting from such a procedure is generally very underdetermined, due to the dimensions of the imaging apparatus, and underconstrained due to the noise in the imaging process. A means by which more information can be obtained is through a form of imaging utilizing code-apertures. Although increasing the amount of information collected, coded-aperture imaging results in a multiplexing of the data. Demultiplexing the data requires a reconstruction process not required in conventional nuclear medicine imaging. The reconstruction process requires the optimization of an estimate to the object to be reconstructed. This optimization is done through the minimization of an energy functional. The minimization of such energy functionals requires the optimization of several parameters. Solution of this type problem is difficult because there are far too many degrees of freedom to permit an exhaustive search for an optimum, and in many cases no algorithms are known which will determine the exact optimum with significantly less work than exhaustive search. Instead, heuristic algorithms, such as the simulated annealing algorithm, have been employed and have proven effective in minimizing such energy functionals. Unfortunately, the simulated annealing algorithm, as characteristic of Monte Carlo algorithms, is very computer intensive; in fact, it is so intensive that insufficient computational power is often the chief hindrance to investigation of the algorithm. The simulated annealing algorithm, however, is amenable to parallel processing. The goal of the research in this dissertation is to investigate the parameters involved in implementing the simulated annealing algorithm in parallel; however, the form of the simulated annealing algorithm implemented here requires no annealing because the energy functionals investigated are quadratic in form. The parameters related to the parallelization of the simulated annealing algorithm include the decomposition of the reconstruction space among the processors, the formulation of the problem at the estimate level with the smallest task being a single perturbation trial evaluated on a local basis, and the communications required to keep all the processors as current as possible with changes made simultaneously to the estimate. Three objects, varying in size, shape and detail, are reconstructed utilizing the TRIMM parallel processor.

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Wade, A. S. C. "Developments of the simulated annealing algorithm." Thesis, University of East Anglia, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300076.

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Norgren, Eric, and Johan Jonasson. "Investigating a Genetic Algorithm-Simulated Annealing Hybrid Applied to University Course Timetabling Problem : A Comparative Study Between Simulated Annealing Initialized with Genetic Algorithm, Genetic Algorithm and Simulated Annealing." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186364.

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Every semester universities around the world have to create new schedules. This task can be very complex considering that a number of constraints has to be taken into account, e.g. there should not exist any timetable clashes for students and a room cannot be double-booked. This can be very hard and time-consuming for a human to do by hand, which is why methods to automate this problem, the University Course Timetabling Problem, has been researched for many years. This report investigates the performance of a hybrid consisting of Genetic Algorithm and Simulated Annealing when solving the University Course Timetabling Problem. An implementation by Yamazaki & Pertoft (2014) was used for the Genetic Algorithm. Simulated Annealing used the Genetic Algorithm as base for its implementation. The hybrid runs the Genetic Algorithm until some breakpoint, takes the best timetable and uses it as an initial solution for the Simulated Annealing. Our results show that our implementation of Simulated Annealing performs better than the hybrid and magnitudes better than the Genetic Algorithm. We believe one reason for this is that the dataset used was too simple, the Genetic Algorithm might scale better as the complexity of the dataset increases.

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Araujo, Haroldo Alexandre de. "Algoritmo Simulated Annealing." Florianópolis, SC, 2001. http://repositorio.ufsc.br/xmlui/handle/123456789/80386.

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Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Ciência da Computação.
Made available in DSpace on 2012-10-18T13:35:55Z (GMT). No. of bitstreams: 1 225675.pdf: 796704 bytes, checksum: 892abc8468e4e7c6715b6c3f2de50e51 (MD5)
A busca por soluções de problemas por meio do computador é o tema central da ciência da computação, relevante para grande parte da ciência e de suas aplicações tecnológicas. Essa busca, certamente, vai na direção de algoritmos eficientes e exatos mas que nem sempre boas soluções podem ser encontradas para muitos problemas de ordem prática, principalmente, no que diz respeito a tempo de execução. Existem problemas, dentre estes, os de otimização combinatorial que apresentam uma peculiaridade com relação aos outros, que é a grande dificuldade de se obter soluções exatas num tempo computacional aceitável. Atualmente, as novas técnicas, especialmente as metaheurísticas, tais como: Tabu Search, Simulated Annealing, Algoritmos Genéticos e Redes Neurais, vêm conseguindo sucesso na solução de problemas de otimização combinatorial, que mesmo não apresentando soluções exatas têm mostrado bastante eficiência com suas soluções aproximadas. Este trabalho propõe um novo método baseado no algoritmo Simulated Annealing (SA) através de mudanças bruscas nos valores da temperatura que são retiradas de múltiplas faixas, ao contrário do SA básico, onde esses valores são obtidos de uma faixa única, ou seja, num SA básico, os valores assumidos pela temperatura saem de um intervalo, partindo de um valor inicial, e vão diminuindo até um valor final. Tais mudanças bruscas acontecem exatamente no momento da mudança de faixa, pois o valor da temperatura que no final de uma faixa é pequeno, assume um valor correspondente a temperatura inicial da faixa seguinte, normalmente, bem maior. Posto a prova, com instâncias euclidianas do Problema Caixeiro Viajante, que é um problema de otimização combinatorial de difícil solução, o método apresenta resultados bastante satisfatórios quando comparado com o SA básico.

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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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Sohn, Eugene. "Simulated annealing algorithm for customer-centric location routing problem." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117923.

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Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 36-38).
In today's world, the e-commerce market is growing rapidly and becoming more competitive. While many players in the industry are attempting to get their share of pie, consumers are demanding faster deliveries and free shipping. This market growth and change in consumer behavior provides an exciting opportunity for companies to compete. In order to meet the new consumer demand, companies need to find better ways to deliver faster. Faster delivery times can be achieved by using an optimization model to plan delivery network and operations. Typically, this optimization model has been based on minimizing cost. However, in the current market, lowest cost is not necessarily the best driver of sales as the consumer culture enters an era of instant gratification. We argue that minimizing customer waiting time will bring better performance and win over market share by providing the quickest delivery service that is expected by the majority of consumers. We propose solving the location routing problem (LRP) aiming at minimizing customer waiting time with capacitated depots and vehicles. We take two approaches to solve this problem: mathematical model and heuristic algorithm. The mathematical model obtains the optimal solution, but it has a limitation on the size of the problem due to the NP-hardness of the LRP. Therefore, we introduce three different variations of Simulated Annealing (SA) algorithm to solve the Capacitated Latency Location Routing Problem (CLLRP). According to the comparison results on a popular benchmark test, one of the designed SAs, the Iterative Simulated Annealing algorithm, consistently provides the best combination of performance and computation time compared to the other two SAs. Therefore, this specific algorithm is further compared to the mathematical model on some problem instances. The comparison results demonstrate that the proposed algorithm performs competitively with the algorithms in the literature and the mathematical model.
by Eugene Sohn.
M. Eng. in Supply Chain Management

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Fleischer, Mark Alan. "Assessing the performance of the simulated annealing algorithm using information theory." Case Western Reserve University School of Graduate Studies / OhioLINK, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=case1057677595.

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Gelfand, Saul B. (Saul Brian). "Analysis of simulated annealing type algorithms." Thesis, Massachusetts Institute of Technology, 1987. http://hdl.handle.net/1721.1/14935.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1987.
MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING
Bibliography: leaves 101-103.
by Saul B. Gelfand.
Ph.D.

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Kovàcs, Akos. "Solving the Vehicle Routing Problem with Genetic ALgorithm and Simulated Annealing." Thesis, Högskolan Dalarna, Datateknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3306.

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This Thesis Work will concentrate on a very interesting problem, the Vehicle Routing Problem (VRP). In this problem, customers or cities have to be visited and packages have to be transported to each of them, starting from a basis point on the map. The goal is to solve the transportation problem, to be able to deliver the packages-on time for the customers,-enough package for each Customer,-using the available resources- and – of course - to be so effective as it is possible.Although this problem seems to be very easy to solve with a small number of cities or customers, it is not. In this problem the algorithm have to face with several constraints, for example opening hours, package delivery times, truck capacities, etc. This makes this problem a so called Multi Constraint Optimization Problem (MCOP). What’s more, this problem is intractable with current amount of computational power which is available for most of us. As the number of customers grow, the calculations to be done grows exponential fast, because all constraints have to be solved for each customers and it should not be forgotten that the goal is to find a solution, what is best enough, before the time for the calculation is up. This problem is introduced in the first chapter: form its basics, the Traveling Salesman Problem, using some theoretical and mathematical background it is shown, why is it so hard to optimize this problem, and although it is so hard, and there is no best algorithm known for huge number of customers, why is it a worth to deal with it. Just think about a huge transportation company with ten thousands of trucks, millions of customers: how much money could be saved if we would know the optimal path for all our packages.Although there is no best algorithm is known for this kind of optimization problems, we are trying to give an acceptable solution for it in the second and third chapter, where two algorithms are described: the Genetic Algorithm and the Simulated Annealing. Both of them are based on obtaining the processes of nature and material science. These algorithms will hardly ever be able to find the best solution for the problem, but they are able to give a very good solution in special cases within acceptable calculation time.In these chapters (2nd and 3rd) the Genetic Algorithm and Simulated Annealing is described in details, from their basis in the “real world” through their terminology and finally the basic implementation of them. The work will put a stress on the limits of these algorithms, their advantages and disadvantages, and also the comparison of them to each other.Finally, after all of these theories are shown, a simulation will be executed on an artificial environment of the VRP, with both Simulated Annealing and Genetic Algorithm. They will both solve the same problem in the same environment and are going to be compared to each other. The environment and the implementation are also described here, so as the test results obtained.Finally the possible improvements of these algorithms are discussed, and the work will try to answer the “big” question, “Which algorithm is better?”, if this question even exists.

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Batts, William Merle. "Modeling of a hardware VLSI placement system : accelerating the simulated annealing algorithm /." Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/1015.

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Books on the topic "The Simulated Annealing Algorithm"

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

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Jones, Mark. An improved simulated annealing algorithm for standard cell placement. Urbana, IL: Computer Systems Group, Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, 1988.

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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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Coleman, Thomas F. A parallel build-up algorithm for global energy minimizations of molecular clusters using effective energy simulated annealing. Ithaca, N.Y: Cornell Theory Center, Cornell University, 1993.

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D, Karaboga, ed. Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks. London: Springer London, 2000.

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1965-, Karaboga Dervis, ed. Intelligent optimisation techniques: Genetic algorithms, tabu search, simulated annealing and neural networks. London: Springer, 2000.

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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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Tucci, Mario, and Marco Garetti, eds. Proceedings of the third International Workshop of the IFIP WG5.7. Florence: Firenze University Press, 2002. http://dx.doi.org/10.36253/88-8453-042-3.

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Contents of the papers presented at the international workshop deal with the wide variety of new and computer-based techniques for production planning and control that has become available to the scientific and industrial world in the past few years: formal modeling techniques, artificial neural networks, autonomous agent theory, genetic algorithms, chaos theory, fuzzy logic, simulated annealing, tabu search, simulation and so on. The approach, while being scientifically rigorous, is focused on the applicability to industrial environment.

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Vidal, René V. V., ed. Applied Simulated Annealing. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-46787-5.

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R. H. J. M. Otten. The Annealing Algorithm. Boston, MA: Springer US, 1989.

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Book chapters on the topic "The Simulated Annealing Algorithm"

Rossmanith, Peter. "Simulated Annealing." In Algorithms Unplugged, 393–400. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-15328-0_41.

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Sechen, Carl. "The Simulated Annealing Algorithm." In The Kluwer International Series in Engineering and Computer Science, 31–49. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4613-1697-8_2.

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van Laarhoven, Peter J. M., and Emile H. L. Aarts. "Towards implementing the algorithm." In Simulated Annealing: Theory and Applications, 55–75. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-015-7744-1_5.

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van Laarhoven, Peter J. M., and Emile H. L. Aarts. "Performance of the simulated annealing algorithm." In Simulated Annealing: Theory and Applications, 77–98. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-015-7744-1_6.

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Czech, Zbigniew J., Wojciech Mikanik, and Rafał Skinderowicz. "Implementing a Parallel Simulated Annealing Algorithm." In Parallel Processing and Applied Mathematics, 146–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14390-8_16.

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Boixo, Sergio, and Rolando D. Somma. "Quantum Algorithms for Simulated Annealing." In Encyclopedia of Algorithms, 1677–80. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_774.

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Boixo, Sergio, and Rolando D. Somma. "Quantum Algorithms for Simulated Annealing." In Encyclopedia of Algorithms, 1–5. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-27848-8_774-1.

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Tang, Min, and Jin-xiang Dong. "Simulated Annealing Genetic Algorithm for Surface Intersection." In Lecture Notes in Computer Science, 48–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539902_6.

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Zbierski, Maciej. "A Simulated Annealing Algorithm for GPU Clusters." In Parallel Processing and Applied Mathematics, 750–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31464-3_76.

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Line, Shih-Wei, and Ching-Jung Ting. "Simulated Annealing Algorithm for Berth Allocation Problems." In Proceedings of the Institute of Industrial Engineers Asian Conference 2013, 449–56. Singapore: Springer Singapore, 2013. http://dx.doi.org/10.1007/978-981-4451-98-7_54.

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Conference papers on the topic "The Simulated Annealing Algorithm"

Nikiel, Slawomir, and Pawel Dabrowski. "Deployment algorithm using simulated annealing." In Robotics (MMAR). IEEE, 2011. http://dx.doi.org/10.1109/mmar.2011.6031327.

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Shiu Yin Yuen and Chi Kin Chow. "A non-revisiting simulated annealing algorithm." In 2008 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2008. http://dx.doi.org/10.1109/cec.2008.4631046.

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Mingyan Jiang and Yongming Cheng. "Simulated annealing artificial fish swarm algorithm." In 2010 8th World Congress on Intelligent Control and Automation (WCICA 2010). IEEE, 2010. http://dx.doi.org/10.1109/wcica.2010.5554452.

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Yang Weibo and Wang Yuedong. "Improved simulated annealing algorithm for GTSP." In International Conference on Automatic Control and Artificial Intelligence (ACAI 2012). Institution of Engineering and Technology, 2012. http://dx.doi.org/10.1049/cp.2012.1194.

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Jiang, Haibo, Tingwen Xing, and Meng Du. "Source optimization using simulated annealing algorithm." In 7th International Symposium on Advanced Optical Manufacturing and Testing Technologies (AOMATT 2014), edited by Yudong Zhang and Wei Gao. SPIE, 2014. http://dx.doi.org/10.1117/12.2069398.

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Li, Xiaomei. "Protein Folding Based on Simulated Annealing Algorithm." In Third International Conference on Natural Computation (ICNC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icnc.2007.583.

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Zhu, Jingwei, Ting Rui, Husheng Fang, Jinlin Zhang, and Ming Liao. "Simulated annealing ant colony algorithm for QAP." In 2012 8th International Conference on Natural Computation. IEEE, 2012. http://dx.doi.org/10.1109/icnc.2012.6234519.

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"Kernel FCM Based on Simulated Annealing Algorithm." In 2017 the 7th International Workshop on Computer Science and Engineering. WCSE, 2017. http://dx.doi.org/10.18178/wcse.2017.06.019.

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"Kernel FCM Based on Simulated Annealing Algorithm." In 2017 the 7th International Workshop on Computer Science and Engineering. WCSE, 2017. http://dx.doi.org/10.18178/wcse.2017.06.020.

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Yanping, Zhang, and Liu Chao. "Cross Covering Algorithm Based on Simulated Annealing." In 2010 International Conference on Digital Manufacturing and Automation (ICDMA). IEEE, 2010. http://dx.doi.org/10.1109/icdma.2010.112.

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Reports on the topic "The Simulated Annealing Algorithm"

Fleischer, Mark, and Sheldon Jacobson. Scale Invariance Properties in the Simulated Annealing Algorithm. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada442644.

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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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Bui, Thang, Christopher Heigham, Curt Jones, and Tom Leighton. Improving the Performance of the Kernighan-Lin and Simulated Annealing Graph Bisection Algorithms. Fort Belvoir, VA: Defense Technical Information Center, June 1989. http://dx.doi.org/10.21236/ada211914.

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Bandyopadhyay, S., S. K. Pal, and C. A. Murthy. Simulated Annealing Based Pattern Classification. Fort Belvoir, VA: Defense Technical Information Center, May 1998. http://dx.doi.org/10.21236/ada358039.

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Cole, James B., and David H. Gerstman. Image Recovery by Simulated Annealing. Fort Belvoir, VA: Defense Technical Information Center, November 1990. http://dx.doi.org/10.21236/ada230655.

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Rose, Jonathan, Wolfgang Klebsch, and Juergen Wolf. Temperature Measurement of Simulated Annealing Placements. Fort Belvoir, VA: Defense Technical Information Center, January 1987. http://dx.doi.org/10.21236/ada207229.

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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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Tsitsiklis, John N. Markov Chains with Rare Transitions and Simulated Annealing. Fort Belvoir, VA: Defense Technical Information Center, September 1985. http://dx.doi.org/10.21236/ada161598.

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Rose, Jonathan, Wolfgang Klebsch, and Juergen Wolf. Temperature Measurement and Equilibrium Dynamics of Simulated Annealing Placements. Fort Belvoir, VA: Defense Technical Information Center, January 1987. http://dx.doi.org/10.21236/ada207230.

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Nakao, Shinsuke, J. Najita, and Kenzi Karasaki. Sensitivity study on hydraulic well testing inversion using simulated annealing. Office of Scientific and Technical Information (OSTI), November 1997. http://dx.doi.org/10.2172/658166.

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