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Journal articles on the topic 'Simulated Annealing Optimization'

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Published: 4 June 2021
Last updated: 16 February 2022

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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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Dekkers, Anton, and Emile Aarts. "Global optimization and simulated annealing." Mathematical Programming 50, no. 1-3 (March 1991): 367–93. http://dx.doi.org/10.1007/bf01594945.

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12

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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13

Leite, J. P. B., and B. H. V. Topping. "Parallel simulated annealing for structural optimization." Computers & Structures 73, no. 1-5 (October 1999): 545–64. http://dx.doi.org/10.1016/s0045-7949(98)00255-7.

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14

Bohachevsky, Ihor O., Mark E. Johnson, and Myron L. Stein. "Generalized Simulated Annealing for Function Optimization." Technometrics 28, no. 3 (August 1986): 209–17. http://dx.doi.org/10.1080/00401706.1986.10488128.

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15

Press, William H., and Saul A. Teukolsky. "Simulated Annealing Optimization over Continuous Spaces." Computers in Physics 5, no. 4 (1991): 426. http://dx.doi.org/10.1063/1.4823002.

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16

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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17

Kalivas, John H. "Optimization using variations of simulated annealing." Chemometrics and Intelligent Laboratory Systems 15, no. 1 (May 1992): 1–12. http://dx.doi.org/10.1016/0169-7439(92)80022-v.

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18

Romeijn, H. Edwin, and Robert L. Smith. "Simulated annealing for constrained global optimization." Journal of Global Optimization 5, no. 2 (September 1994): 101–26. http://dx.doi.org/10.1007/bf01100688.

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19

Suman, Balram, Nazish Hoda, and Shweta Jha. "Orthogonal simulated annealing for multiobjective optimization." Computers & Chemical Engineering 34, no. 10 (October 2010): 1618–31. http://dx.doi.org/10.1016/j.compchemeng.2009.11.015.

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20

ÖZDAMAR, LINET, and CHANDRA SEKHAR PEDAMALLU. "NEW SIMULATED ANNEALING ALGORITHMS FOR CONSTRAINED OPTIMIZATION." Asia-Pacific Journal of Operational Research 27, no. 03 (June 2010): 347–67. http://dx.doi.org/10.1142/s0217595910002740.

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We propose a Population based dual-sequence Non-Penalty Annealing algorithm (PNPA) for solving the general nonlinear constrained optimization problem. The PNPA maintains a population of solutions that are intermixed by crossover to supply a new starting solution for simulated annealing throughout the search. Every time the search gets stuck at a local optimum, this crossover procedure is triggered and simulated annealing search re-starts from a new subspace. In both the crossover and simulated annealing procedures, the objective function value and the total solution infeasibility degrees are treated as separate performance criteria. Feasible solutions are assessed according to their objective function values and infeasible solutions are assessed with regard to their absolute degree of constraint infeasibility. In other words, in the proposed approach, there exist two sequences of solutions: the feasible sequence and the infeasible sequence. We compare the population based dual sequence PNPA with the standard single sequence Penalty Annealing (the PA), and with the random seed dual sequence Non-Penalty Annealing (NPA). Numerical experiments show that PNPA is more effective than its counterparts.
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21

Ding, Ji Bin. "Simulated Annealing Optimization of Belt Conveyor Transmission." Advanced Materials Research 113-116 (June 2010): 2373–78. http://dx.doi.org/10.4028/www.scientific.net/amr.113-116.2373.

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The belt conveyor is a transporting machine by friction in a continuous manner. The two order helical gearing reducer may be generally used as conveyor transmission, and can reduce speed and increase torque of belt. The objective function may be specified that that total center distance of the reducer incline to minimum, so the optimization model including the property and boundary constraints is created. Then the objective function with penalty terms is converted by penalty strategy with addition type, so as to transform the constrained optimization into the unconstrained optimization model. Considering the problem of low efficiency and local optimum caused by standard optimization methods, the simulated annealing algorithm is adopted to solve the optimization model of Belt Conveyor Transmission, and neural network method is applied to fit relative coefficient, then BFGS Quasi-Newton method is recalled automatically when the setting working precision is achieved again. So that the optimization process is simplified and global optimum is acquired reliably.
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22

Brauer, Hartmut, and Marek Ziolkowski. "Magnet shape optimization using adaptive simulated annealing." Facta universitatis - series: Electronics and Energetics 19, no. 2 (2006): 165–72. http://dx.doi.org/10.2298/fuee0602165b.

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Stochastic methods offer a certain robustness quality to the optimization process. In this paper, the Adaptive Simulated Annealing (ASA) searching techniques are applied to the shape optimization of an electromagnet. The magnetic field is computed using the 2D finite element code FEMM. The aim of optimization is the search for an optimal pole shape geometry leading to a homogeneous magnetic field distribution in the region of interest.
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23

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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24

Zhang, Jin Hua. "Research of Improved Simulated Annealing Optimization Algorithm Based on the Global Harmony Search Mechanism." Advanced Materials Research 482-484 (February 2012): 2500–2503. http://dx.doi.org/10.4028/www.scientific.net/amr.482-484.2500.

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The traditional simulated annealing algorithm adopts serial optimization structure, and it has the defects of slow annealing process, low computational efficiency and so on. On the basis of the traditional simulated annealing algorithm, the improved simulated annealing optimization algorithm with the global harmony search Mechanism is put forward, which can quicken convergence rate and raise search efficiency at the same time of ensuring algorithm to fall in the global optimal solution. In this paper, by using conventional optimization algorithm, traditional simulated annealing algorithm and the improved optimization algorithm to optimize the famous global optimization function, the results show that the improved optimization algorithm has the advantages of high computational efficiency, high computational accuracy, etc.
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25

Oktavian, Rama, Agung Ari Wibowo, and Zuraidah Fitriah. "Study on Particle Swarm Optimization Variant and Simulated Annealing in Vapor Liquid Equilibrium Calculation." Reaktor 19, no. 2 (August 11, 2019): 77–83. http://dx.doi.org/10.14710/reaktor.19.2.77-83.

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Phase equilibrium calculation plays a major rule in optimization of separation process in chemical processing. Phase equilibrium calculation is still very challenging due to highly nonlinear and non-convex of mathematical models. Recently, stochastic optimization method has been widely used to solve those problems. One of the promising stochastic methods is Particle Swarm Optimization (PSO) due to its simplicity and robustness. This study presents the capability of particle swarm optimization for correlating isothermal vapor liquid equilibrium data of water with methanol and ethanol system by optimizing Wilson, Non-Random Two Liquids (NRTL), and Universal Quasi Chemical (UNIQUAC) activity coefficient model and also presents the comparison with bare-bones PSO (BBPSO) and simulated annealing (SA). Those three optimization methods were successfully tested and validated to model vapor liquid equilibrium calculation and were successfully applied to correlate vapor liquid equilibrium data for those types of systems with deviation less than 2%. In addition, BBPSO shows a consistency result and faster convergence among those three optimization methods. Keywords: Phase equilibrium, stochastic method, particle swarm optimization, simulated annealing and activity coefficient model
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26

Wang, Lei, and Yongqiang Liu. "Application of Simulated Annealing Particle Swarm Optimization Based on Correlation in Parameter Identification of Induction Motor." Mathematical Problems in Engineering 2018 (July 8, 2018): 1–9. http://dx.doi.org/10.1155/2018/1869232.

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The strengths and weaknesses of correlation algorithm, simulated annealing algorithm, and particle swarm optimization algorithm are studied in this paper. A hybrid optimization algorithm is proposed by drawing upon the three algorithms, and the specific application processes are given. To extract the current fundamental signal, the correlation algorithm is used. To identify the motor dynamic parameter, the filtered stator current signal is simulated using simulated annealing particle swarm algorithm. The simulated annealing particle swarm optimization algorithm effectively incorporates the global optimization ability of simulated annealing algorithm with the fast convergence of particle swarm optimization by comparing the identification results of asynchronous motor with constant torque load and step load.
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27

Zhang, Xiao Hong, and Jiang Tao Zhang. "Optimization of Aluminum Foam by Simulated Annealing." Advanced Materials Research 573-574 (October 2012): 1187–92. http://dx.doi.org/10.4028/www.scientific.net/amr.573-574.1187.

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According to two parameters combination of the material thickness and air-gap depth, from the angle of pure optimization, this paper obtained the optimal parameters of single cavity and double cavity structure by using the simulated annealing algorithm for single cavity and double cavity of aluminum foam sound absorption structure of systematically optimization design at 100-4000HZ frequency band. Finally, studying effect of increasing the number of cavity on aluminum foam sound absorption properties.
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28

Wang, Xue Ni, and Jing Zhou. "Application of Simulated Annealing Particle Swarm Optimization in Response Spectrum Fitting of Simulated Earthquake Wave." Applied Mechanics and Materials 444-445 (October 2013): 1082–86. http://dx.doi.org/10.4028/www.scientific.net/amm.444-445.1082.

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In order to get a simulated earthquake wave whose response spectrum fitted well to the smooth design response spectrum, a model was established by making the standard error between the response spectrum of simulated earthquake wave and the design response spectrum as the minimal optimization objective. Simulated annealing particle swarm optimization algorithm, which was an improvement algorithm of particle swarm optimization, was used to solve the model. This spectrum fitting method was compared with the conventional spectrum fitting method, which adjusted Fourier amplitude spectrum in frequency domain. The results show that the method of response spectrum fitting by applying simulated annealing particle swarm optimization algorithm has a good convergence. And the response spectrum of simulated earthquake wave generated by simulated annealing particle swarm optimization algorithm agrees better with the design response spectrum than that by conventional spectrum fitting method.
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29

Moh, Jau-Sung, and Dar-Yun Chiang. "Improved Simulated Annealing Search for Structural Optimization." AIAA Journal 38, no. 10 (October 2000): 1965–73. http://dx.doi.org/10.2514/2.852.

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30

Im, Jongbin, Sang-Hyun Ji, and Jungsun Park. "Optimization of Satellite Structures by Simulated Annealing." Transactions of the Korean Society of Mechanical Engineers A 29, no. 2 (February 1, 2005): 262–69. http://dx.doi.org/10.3795/ksme-a.2005.29.2.262.

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31

Rodríguez, Diego A., Paola P. Oteiza, and Nélida B. Brignole. "Simulated Annealing Optimization for Hydrocarbon Pipeline Networks." Industrial & Engineering Chemistry Research 52, no. 25 (June 14, 2013): 8579–88. http://dx.doi.org/10.1021/ie400022g.

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32

Pan, Xiuqin, Limiao Xue, Yong Lu, and Na Sun. "Hybrid particle swarm optimization with simulated annealing." Multimedia Tools and Applications 78, no. 21 (September 26, 2018): 29921–36. http://dx.doi.org/10.1007/s11042-018-6602-4.

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33

Kim, Ho‐Gyun, Chang‐Ok Bae, and Dong‐Jun Park. "Reliability‐redundancy optimization using simulated annealing algorithms." Journal of Quality in Maintenance Engineering 12, no. 4 (October 2006): 354–63. http://dx.doi.org/10.1108/13552510610705928.

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34

Alkhamis, Talal M., Mohamed A. Ahmed, and Vu Kim Tuan. "Simulated annealing for discrete optimization with estimation." European Journal of Operational Research 116, no. 3 (August 1999): 530–44. http://dx.doi.org/10.1016/s0377-2217(98)00112-x.

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35

SUPPAPITNARM, A., K. A. SEFFEN, G. T. PARKS, and P. J. CLARKSON. "A SIMULATED ANNEALING ALGORITHM FOR MULTIOBJECTIVE OPTIMIZATION." Engineering Optimization 33, no. 1 (November 2000): 59–85. http://dx.doi.org/10.1080/03052150008940911.

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36

ZHANG, CHUN, and HSU-PIN (BEN) WANG. "MIXED-DISCRETE NONLINEAR OPTIMIZATION WITH SIMULATED ANNEALING." Engineering Optimization 21, no. 4 (September 1993): 277–91. http://dx.doi.org/10.1080/03052159308940980.

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37

Stochino, Flavio, and Fernando Lopez Gayarre. "Reinforced Concrete Slab Optimization with Simulated Annealing." Applied Sciences 9, no. 15 (August 3, 2019): 3161. http://dx.doi.org/10.3390/app9153161.

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Flat slabs have several advantages such as a reduced and simpler formwork, versatility, and easier space partitioning, thus making them an economical and efficient structural system. When producing structural components in series, every detail can lead to significant cost differences. In these cases, structural optimization is of paramount relevance. This paper reports on the structural optimization of reinforced concrete slabs, presenting the case of a rectangular slab with two clamped adjacent edges and two simply supported edges. Using the yield lines method and the principle of virtual work, a cost function can be formulated and optimized using simulated annealing (SA). Thus, the optimal distribution of reinforcing bars and slab thickness can be found considering the flexural ultimate limit state and market materials costs. The optimum result was defined by the orthotropic coefficient k = 8, anisotropic coefficient g = 1.4, and slab thickness H = 11.8 cm. A sensitivity analysis of the solution was developed considering different material costs.
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38

Moh, Jau-Sung, and Dar-Yun Chiang. "Improved simulated annealing search for structural optimization." AIAA Journal 38 (January 2000): 1965–73. http://dx.doi.org/10.2514/3.14635.

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39

Moret, M. A., P. G. Pascutti, P. M. Bisch, and K. C. Mundim. "Stochastic molecular optimization using generalized simulated annealing." Journal of Computational Chemistry 19, no. 6 (April 30, 1998): 647–57. http://dx.doi.org/10.1002/(sici)1096-987x(19980430)19:6<647::aid-jcc6>3.0.co;2-r.

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40

Morrill, S. M., R. G. Lane, G. Jacobson, and I. I. Rosen. "Treatment planning optimization using constrained simulated annealing." Physics in Medicine and Biology 36, no. 10 (October 1, 1991): 1341–61. http://dx.doi.org/10.1088/0031-9155/36/10/004.

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41

Hoffmann, Karl Heinz, and Peter Salamon. "Simulated annealing for single minimum optimization problems." International Journal of Computer Mathematics 39, no. 3-4 (January 1991): 193–204. http://dx.doi.org/10.1080/00207169108803991.

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42

Spinellis, D., C. Papadopoulos, and J. MaCgregor Smith. "Large production line optimization using simulated annealing." International Journal of Production Research 38, no. 3 (February 2000): 509–41. http://dx.doi.org/10.1080/002075400189284.

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43

Lin, Chyi‐Yeu, and Feng‐Heh Wang. "Sequential simulated annealing for multimodal design optimization." Journal of the Chinese Institute of Engineers 26, no. 1 (January 2003): 57–70. http://dx.doi.org/10.1080/02533839.2003.9670754.

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44

Gonzalez, J., I. Rojas, H. Pomares, M. Salmeron, and J. J. Merelo. "Web newspaper layout optimization using simulated annealing." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 32, no. 5 (October 2002): 686–91. http://dx.doi.org/10.1109/tsmcb.2002.1033189.

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45

Gelfand, Saul B., and Sanjoy K. Mitter. "Simulated annealing type algorithms for multivariate optimization." Algorithmica 6, no. 1-6 (June 1991): 419–36. http://dx.doi.org/10.1007/bf01759052.

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46

Johnson, S. Andrew, Dinesh P. Mehta, and Ramakrishna Thurimella. "Identifying algorithmic vulnerabilities through simulated annealing." Optimization Letters 5, no. 3 (May 8, 2011): 479–90. http://dx.doi.org/10.1007/s11590-011-0333-z.

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47

Trouvé, Alain. "Cycle Decompositions and Simulated Annealing." SIAM Journal on Control and Optimization 34, no. 3 (May 1996): 966–86. http://dx.doi.org/10.1137/s0363012993258586.

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48

Almarashi, Majid, Wael Deabes, Hesham H. Amin, and Abdel-Rahman Hedar. "Simulated Annealing with Exploratory Sensing for Global Optimization." Algorithms 13, no. 9 (September 12, 2020): 230. http://dx.doi.org/10.3390/a13090230.

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Simulated annealing is a well-known search algorithm used with success history in many search problems. However, the random walk of the simulated annealing does not benefit from the memory of visited states, causing excessive random search with no diversification history. Unlike memory-based search algorithms such as the tabu search, the search in simulated annealing is dependent on the choice of the initial temperature to explore the search space, which has little indications of how much exploration has been carried out. The lack of exploration eye can affect the quality of the found solutions while the nature of the search in simulated annealing is mainly local. In this work, a methodology of two phases using an automatic diversification and intensification based on memory and sensing tools is proposed. The proposed method is called Simulated Annealing with Exploratory Sensing. The computational experiments show the efficiency of the proposed method in ensuring a good exploration while finding good solutions within a similar number of iterations.
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49

El Alem, W., A. El Hami, and Rachid Ellaia. "Structural Shape Optimization Using an Adaptive Simulated Annealing." Key Engineering Materials 446 (July 2010): 101–10. http://dx.doi.org/10.4028/www.scientific.net/kem.446.101.

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In structural design optimization, numerical techniques are increasingly used. In typical structural optimization problems there may be many locally minimum configurations. For that reason, the application of a global method, which may escape from the locally minimum points, remain essential. In this paper, a new hybrid simulated annealing algorithm for global optimization with constraints is proposed. We have developed a new algorithm called Adaptive Simulated Annealing algorithm (ASA); ASA is a series of modifications done to the Basic Simulated Annealing algorithm ( BSA) that gives the region containing the global solution of an objective function. In addition, the stochastic method Simultaneous Perturbation Stochastic Approximation (SPSA), for solving unconstrained optimization problems, is used to refine the solution. We also propose Penalty SPSA (PSPSA) for solving constrained optimization problems. The constraints are handled using exterior point penalty functions. The proposed method is applicable for any problem where the topology of the structure is not fixed, it is simple and capable of handling problems subject to any number of nonlinear constraints. Extensive tests on the ASA as a global optimization method are presented, its performance as a viable optimization method is demonstrated by applying it first to a series of benchmark functions with 2 - 30 dimensions and then it is used in structural design to demonstrate its applicability and efficiency. It is found that the best results are obtained by ASA compared to those provided by the commercial software ANSYS.
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50

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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