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Journal articles on the topic 'Simulated annealing'

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Published: 4 June 2021
Last updated: 6 September 2023

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1

Fabian, V. "Simulated annealing simulated." Computers & Mathematics with Applications 33, no. 1-2 (January 1997): 81–94. http://dx.doi.org/10.1016/s0898-1221(96)00221-0.

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2

Tovey, Craig A. "Simulated Simulated Annealing." American Journal of Mathematical and Management Sciences 8, no. 3-4 (February 1988): 389–407. http://dx.doi.org/10.1080/01966324.1988.10737246.

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3

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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4

KITA, Hajime. "Simulated Annealing." Journal of Japan Society for Fuzzy Theory and Systems 9, no. 6 (1997): 870–75. http://dx.doi.org/10.3156/jfuzzy.9.6_870.

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5

Bertsimas, Dimitris, and John Tsitsiklis. "Simulated Annealing." Statistical Science 8, no. 1 (February 1993): 10–15. http://dx.doi.org/10.1214/ss/1177011077.

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6

Johnson, Mark E. "Simulated Annealing." American Journal of Mathematical and Management Sciences 8, no. 3-4 (February 1988): 205–7. http://dx.doi.org/10.1080/01966324.1988.10737241.

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7

Bhattacharya, Arijit, Sayantan Ghatak, Rajib Das, and Satrajit Ghosh. "Simulated Annealing Approach onto VLSI Circuit Partitioning." Mathematical Journal of Interdisciplinary Sciences 2, no. 2 (March 3, 2014): 133–39. http://dx.doi.org/10.15415/mjis.2014.22010.

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8

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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9

O. H. Abdelwahed, O. H. Abdelwahed, and M. El-Sayed Wahed. "Optimizing Single Layer Cellular Neural Network Simulator using Simulated Annealing Technique with Neural Networks." Indian Journal of Applied Research 3, no. 6 (October 1, 2011): 91–94. http://dx.doi.org/10.15373/2249555x/june2013/31.

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10

Kallab, Chadi, Samir Haddad, Jinane Sayah, and Mohamad Chakroun. "Generic Simulated Annealing." Open Journal of Applied Sciences 12, no. 06 (2022): 1011–25. http://dx.doi.org/10.4236/ojapps.2022.126069.

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11

Silverman, Amihai, and Joan Adler. "Animated Simulated Annealing." Computers in Physics 6, no. 3 (1992): 277. http://dx.doi.org/10.1063/1.4823076.

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12

Xavier-de-Souza, S., J. A. K. Suykens, J. Vandewalle, and D. Bolle. "Coupled Simulated Annealing." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 40, no. 2 (April 2010): 320–35. http://dx.doi.org/10.1109/tsmcb.2009.2020435.

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13

Tsallis, Constantino, and Daniel A. Stariolo. "Generalized simulated annealing." Physica A: Statistical Mechanics and its Applications 233, no. 1-2 (November 1996): 395–406. http://dx.doi.org/10.1016/s0378-4371(96)00271-3.

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14

Yao, Xin, and Guojie Li. "General simulated annealing." Journal of Computer Science and Technology 6, no. 4 (October 1991): 329–38. http://dx.doi.org/10.1007/bf02948392.

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15

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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16

Fox, Bennett L. "Faster Simulated Annealing." SIAM Journal on Optimization 5, no. 3 (August 1995): 488–505. http://dx.doi.org/10.1137/0805025.

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17

Kvasnička, V., and J. Pospíchal. "Messy simulated annealing." Journal of Chemometrics 9, no. 4 (July 1995): 309–22. http://dx.doi.org/10.1002/cem.1180090405.

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18

Mazza, Christian. "Parallel simulated annealing." Random Structures & Algorithms 3, no. 2 (1992): 139–48. http://dx.doi.org/10.1002/rsa.3240030204.

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19

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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20

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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21

Poorjafari, Vahid, Wen Long Yue, and Nicholas Holyoak. "Application of Simulated Annealing in Transit Schedule Synchronization." International Journal of Modeling and Optimization 4, no. 6 (December 2014): 476–81. http://dx.doi.org/10.7763/ijmo.2014.v4.420.

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22

Roh, Ji-Eun, Chang-Soo Ahn, Seon-Joo Kim, Dae-Sung Jang, and Han-Lim Choi. "Stochastic Radar Beam Scheduling Using Simulated Annealing." Journal of Korean Institute of Electromagnetic Engineering and Science 23, no. 2 (February 29, 2012): 196–206. http://dx.doi.org/10.5515/kjkiees.2012.23.2.196.

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23

Nishimori, H. "Comparison of quantum annealing and simulated annealing." European Physical Journal Special Topics 224, no. 1 (February 2015): 15–16. http://dx.doi.org/10.1140/epjst/e2015-02338-0.

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24

Monmarché, Pierre. "Piecewise deterministic simulated annealing." Latin American Journal of Probability and Mathematical Statistics 13, no. 1 (2016): 357. http://dx.doi.org/10.30757/alea.v13-15.

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25

Connolly, David. "General Purpose Simulated Annealing." Journal of the Operational Research Society 43, no. 5 (May 1992): 495. http://dx.doi.org/10.2307/2583568.

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26

Brunger, A. T. "Simulated Annealing in Crystallography." Annual Review of Physical Chemistry 42, no. 1 (October 1991): 197–223. http://dx.doi.org/10.1146/annurev.pc.42.100191.001213.

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27

Zomaya, A. Y. "Natural and simulated annealing." Computing in Science & Engineering 3, no. 6 (November 2001): 97–99. http://dx.doi.org/10.1109/mcise.2001.963434.

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28

Aarts, E. H. L., and P. J. M. Laarhoven. "Simulated annealing: An introduction." Statistica Neerlandica 43, no. 1 (March 1989): 31–52. http://dx.doi.org/10.1111/j.1467-9574.1989.tb01245.x.

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29

Thompson, D. R., and G. L. Bilbro. "Sample-Sort Simulated Annealing." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 35, no. 3 (June 2005): 625–32. http://dx.doi.org/10.1109/tsmcb.2005.843972.

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30

Connolly, David. "General Purpose Simulated Annealing." Journal of the Operational Research Society 43, no. 5 (May 1992): 495–505. http://dx.doi.org/10.1057/jors.1992.75.

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31

Wang, L., and K. Smith. "On chaotic simulated annealing." IEEE Transactions on Neural Networks 9, no. 4 (July 1998): 716–18. http://dx.doi.org/10.1109/72.701185.

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32

MIKI, Mitsunori, Tomoyuki HIROYASU, Keiko ONO, Takeshi YOSHIDA, and Komei KUBOTA. "332 Adaptive Simulated Annealing." Proceedings of The Computational Mechanics Conference 2001.14 (2001): 325–26. http://dx.doi.org/10.1299/jsmecmd.2001.14.325.

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33

Greening, Daniel R. "Parallel simulated annealing techniques." Physica D: Nonlinear Phenomena 42, no. 1-3 (June 1990): 293–306. http://dx.doi.org/10.1016/0167-2789(90)90084-3.

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34

Montoya, F., and J. M. Dubois. "Darwinian Adaptative Simulated Annealing." Europhysics Letters (EPL) 22, no. 2 (April 10, 1993): 79–84. http://dx.doi.org/10.1209/0295-5075/22/2/001.

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35

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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36

Basu, Atanu, and L. Neil Frazer. "Inversion using simulated annealing." Journal of the Acoustical Society of America 86, S1 (November 1989): S52. http://dx.doi.org/10.1121/1.2027547.

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37

Ram, D. Janaki, T. H. Sreenivas, and K. Ganapathy Subramaniam. "Parallel Simulated Annealing Algorithms." Journal of Parallel and Distributed Computing 37, no. 2 (September 1996): 207–12. http://dx.doi.org/10.1006/jpdc.1996.0121.

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38

Walsh, P. A., and D. M. Miller. "Goal-directed simulated annealing and simulated sintering." Microelectronics Journal 25, no. 5 (August 1994): 363–82. http://dx.doi.org/10.1016/0026-2692(94)90084-1.

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39

Cicirello, Vincent. "Variable Annealing Length and Parallelism in Simulated Annealing." Proceedings of the International Symposium on Combinatorial Search 8, no. 1 (September 1, 2021): 2–10. http://dx.doi.org/10.1609/socs.v8i1.18424.

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In this paper, we propose: (a) a restart schedule for an adaptive simulated annealer, and (b) parallel simulated annealing, with an adaptive and parameter-free annealing schedule. The foundation of our approach is the Modified Lam annealing schedule, which adaptively controls the temperature parameter to track a theoretically ideal rate of acceptance of neighboring states. A sequential implementation of Modified Lam simulated annealing is almost parameter-free. However, it requires prior knowledge of the annealing length. We eliminate this parameter using restarts, with an exponentially increasing schedule of annealing lengths. We then extend this restart schedule to parallel implementation, executing several Modified Lam simulated annealers in parallel, with varying initial annealing lengths, and our proposed parallel annealing length schedule. To validate our approach, we conduct experiments on an NP-Hard scheduling problem with sequence-dependent setup constraints. We compare our approach to fixed length restarts, both sequentially and in parallel. Our results show that our approach can achieve substantial performance gains, throughout the course of the run, demonstrating our approach to be an effective anytime algorithm.
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40

Rajasekaran, Sanguthevar, and John H. Reif. "Nested annealing: a provable improvement to simulated annealing." Theoretical Computer Science 99, no. 1 (June 1992): 157–76. http://dx.doi.org/10.1016/0304-3975(92)90177-h.

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41

Jeon, Eo-Jin, Young-Hwan Kim, Ji-Hoon Park, and Man-Pil Kim. "Development of forest carbon optimization program using simulated annealing heuristic algorithm." Journal of the Korea Society of Computer and Information 18, no. 12 (December 31, 2013): 197–205. http://dx.doi.org/10.9708/jksci.2013.18.12.197.

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42

Han, Keun-Hee, and Yong-Jin Lee. "Study on the L(2,1)-labeling problem based on simulated annealing algorithm." Journal of Korean Institute of Intelligent Systems 21, no. 1 (February 25, 2011): 138–44. http://dx.doi.org/10.5391/jkiis.2011.21.1.138.

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43

NOLTE, ANDREAS, and RAINER SCHRADER. "Simulated Annealing and Graph Colouring." Combinatorics, Probability and Computing 10, no. 1 (January 2001): 29–40. http://dx.doi.org/10.1017/s0963548300004557.

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Simulated annealing is a very successful heuristic for various problems in combinatorial optimization. In this paper an application of simulated annealing to the 3-colouring problem is considered. In contrast to many good empirical results we will show for a certain class of graphs that the expected first hitting time of a proper colouring, given an arbitrary cooling scheme, is of exponential size.These results are complementary to those in [13], where we prove the convergence of simulated annealing to an optimal solution in exponential time.
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44

Venkateswaran, C., M. Ramachandran, Ramu Kurinjimalar, Prasanth Vidhya, and G. Mathivanan. "Application of Simulated Annealing in Various Field." Materials and its Characterization 1, no. 1 (February 1, 2022): 01–08. http://dx.doi.org/10.46632/mc/1/1/1.

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Simulated annealing is a method of solving uncontrolled and controlled optimization problems. This method simulates the physical process of heating an object and then slowly lowering the temperature to minimize defects, thus reducing system power. Simulated Annealing is a Constant global search Is the optimization algorithm. The algorithm is attracted by annealing in metallurgy, where the metal is rapidly heated to a high temperature and then slowly cooled, which increases its strength and makes it easier to work with. Implements simulated anal search in the same way. With each repetition in the Simulated Annealing Algorithm, a new point Created approx. From the current point Distance to new point or amount of search, Probability distribution that is in proportion to the temperature. All of the algorithm Accepts intent to reduce new points, but will raise the target with a certain probability Accepts points as well. Accept the scope The algorithm that raises the scores avoids getting stuck in the local minima and Explore globally for possible solutions. Algorithm Continuing, to lower the temperature properly, annealing as the temperature drops, algorithm search size reduces and at least integrates
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45

Biju, G. S., and A. K. Anilkumar. "Simulated Annealing with Clone Operator." Global Journal of Pure and Applied Mathematics 13, no. 10 (October 30, 2017): 7601. http://dx.doi.org/10.37622/gjpam/13.10.2017.7601-7625.

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46

Vesga-Ramírez, A., J. D. Sanabria-Gómez, D. Sierra-Porta, L. Arana-Salinas, H. Asorey, V. A. Kudryavtsev, R. Calderón-Ardila, and L. A. Núñez. "Simulated Annealing for volcano muography." Journal of South American Earth Sciences 109 (August 2021): 103248. http://dx.doi.org/10.1016/j.jsames.2021.103248.

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47

Correia, M. Helena, José F. Oliveira, and J. Soeiro Ferreira. "Cylinder packing by simulated annealing." Pesquisa Operacional 20, no. 2 (December 2000): 269–86. http://dx.doi.org/10.1590/s0101-74382000000200009.

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This paper is motivated by the problem of loading identical items of circular base (tubes, rolls, ...) into a rectangular base (the pallet). For practical reasons, all the loaded items are considered to have the same height. The resolution of this problem consists in determining the positioning pattern of the circular bases of the items on the rectangular pallet, while maximizing the number of items. This pattern will be repeated for each layer stacked on the pallet. Two algorithms based on the meta-heuristic Simulated Annealing have been developed and implemented. The tuning of these algorithms parameters implied running intensive tests in order to improve its efficiency. The algorithms developed were easily extended to the case of non-identical circles.
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48

Hwang, Chii-Ruey. "Simulated annealing: Theory and applications." Acta Applicandae Mathematicae 12, no. 1 (May 1988): 108–11. http://dx.doi.org/10.1007/bf00047572.

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49

Goto, Tiago, Hossein R. Najafabadi, Mizael Falheiro, Thiago C. Martins, Ahmad Barari, and Marcos S. G. Tsuzuki. "Topological Optimization and Simulated Annealing." IFAC-PapersOnLine 54, no. 1 (2021): 205–10. http://dx.doi.org/10.1016/j.ifacol.2021.08.078.

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50

Huang, Lin, Chenxi Yu, Hanqi Zhuang, and Salvatore Morgera. "Biometric fusion by simulated annealing." International Journal of Knowledge-based and Intelligent Engineering Systems 16, no. 2 (March 15, 2012): 87–98. http://dx.doi.org/10.3233/kes-2010-0234.

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