Journal articles: 'Simulated Annealing (SA)'

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Siddique, Nazmul, and Hojjat Adeli. "Simulated Annealing, Its Variants and Engineering Applications." International Journal on Artificial Intelligence Tools 25, no. 06 (October 27, 2016): 1630001. http://dx.doi.org/10.1142/s0218213016300015.

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This paper presents a review of research reported on simulated annealing (SA). Different cooling/annealing schedules are summarized. Variants of SA are delineated. Recent applications of SA in engineering are reviewed.

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Yin, Changchun, and Greg Hodges. "Simulated annealing for airborne EM inversion." GEOPHYSICS 72, no. 4 (July 2007): F189—F195. http://dx.doi.org/10.1190/1.2736195.

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The traditional algorithms for airborne electromagnetic (EM) inversion, e.g., the Marquardt-Levenberg method, generally run only a downhill search. Consequently, the model solutions are strongly dependent on the starting model and are easily trapped in local minima. Simulated annealing (SA) starts from the Boltzmann distribution and runs both downhill and uphill searches, rendering the searching process to easily jump out of local minima and converge to a global minimum. In the SA process, the calculation of Jacobian derivatives can be avoided because no preferred searching direction is required as in the case of the traditional algorithms. We apply SA technology for airborne EM inversion by comparing the inversion with a thermodynamic process, and we discuss specifically the SA procedure with respect to model configuration, random walk for model updates, objective function, and annealing schedule. We demonstrate the SA flexibility for starting models by allowing the model parameters to vary in a large range (far away from the true model). Further, we choose a temperature-dependent random walk for model updates and an exponential cooling schedule for the SA searching process. The initial temperature for the SA cooling scheme is chosen differently for different model parameters according to their resolvabilities. We examine the effectiveness of the algorithm for airborne EM by inverting both theoretical and survey data and by comparing the results with those from the traditional algorithms.

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Asrin, Asrin, La Hamimu, and Al Rubaiyn. "Inversi Data Gravitasi Menggunakan Simulated Annealing." Jurnal Rekayasa Geofisika Indonesia 4, no. 03 (November 27, 2022): 141. http://dx.doi.org/10.56099/jrgi.v4i03.28711.

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Inversi data gravitasi penting untuk dilakukan untuk mengetahui model bawah permukaan bumi berdasarkan data anomali medan gravitasi. Inversi data gravitasi dapat dilakukan dengan pendekatan global, Salah satu metode inversi dengan pendekatan global adalah metode simulated annealing (SA). Metode SA ini didasarkan pada analogi proses termodinamika pembentukan kristal suatu substansi. Perkembangannya terinspirasi oleh proses pendinginan logam, dimana struktur kristal energi minimum yang teratur berkembang di dalam logam saat didinginkan secara perlahan dari keadaan panas. Data yang digunakan merupakan data sintetik hasil dari forward modeling untuk menguji keakurasian metode SA. Hasil inversi SA dapat diterapkan dalam menyelesaikan masalah inversi gravitasi dan menunjukkan hasil model inversi yang sesuai dengan model sintetik. Hasil uji coba dengan 2 jenis model sintetik yaitu model persegi dan model patahan menunjukkan nilai misfit estimasi anomali medan gravitasi 2,8 x10-4 mGal untuk model persegi dan 1,7 x 10-4 mGal untuk model patahan. Solusi inversi SA dipengaruhi oleh dua hal, yaitu pembatasan ruang pencarian dan penentuan model tebakan awal.

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Brondani, Marcia De Fatima, Airam Teresa Zago Romcy Sausen, Paulo Sérgio Sausen, and Manuel Osório Binelo. "Battery Model Parameters Estimation Using Simulated Annealing." TEMA (São Carlos) 18, no. 1 (May 22, 2017): 127. http://dx.doi.org/10.5540/tema.2017.018.01.0127.

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In this paper, a Simulated Annealing (SA) algorithm is proposed for the Battery model parametrization, which is used for the mathematical modeling of the Lithium Ion Polymer (LiPo) batteries lifetime. Experimental data obtained by a testbed were used for model parametrization and validation. The proposed SA algorithm is compared to the traditional parametrization methodology that consists in the visual analysis of discharge curves, and from the results obtained, it is possible to see the model efficacy in batteries lifetime prediction, and the proposed SA algorithm efficiency in the parameters estimation.

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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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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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Fatyanosa, Tirana Noor, Andreas Nugroho Sihananto, Gusti Ahmad Fanshuri Alfarisy, M. Shochibul Burhan, and Wayan Firdaus Mahmudy. "Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization." Journal of Information Technology and Computer Science 1, no. 2 (February 8, 2017): 82. http://dx.doi.org/10.25126/jitecs.20161215.

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The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result

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Alkhateeb, Faisal, and Bilal H. Abed-alguni. "A Hybrid Cuckoo Search and Simulated Annealing Algorithm." Journal of Intelligent Systems 28, no. 4 (September 25, 2019): 683–98. http://dx.doi.org/10.1515/jisys-2017-0268.

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Abstract Simulated annealing (SA) proved its success as a single-state optimization search algorithm for both discrete and continuous problems. On the contrary, cuckoo search (CS) is one of the well-known population-based search algorithms that could be used for optimizing some problems with continuous domains. This paper provides a hybrid algorithm using the CS and SA algorithms. The main goal behind our hybridization is to improve the solutions generated by CS using SA to explore the search space in an efficient manner. More precisely, we introduce four variations of the proposed hybrid algorithm. The proposed variations together with the original CS and SA algorithms were evaluated and compared using 10 well-known benchmark functions. The experimental results show that three variations of the proposed algorithm provide a major performance enhancement in terms of best solutions and running time when compared to CS and SA as stand-alone algorithms, whereas the other variation provides a minor enhancement. Moreover, the experimental results show that the proposed hybrid algorithms also outperform some well-known optimization algorithms.

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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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Alsumairat, Nour, and Mahmoud Alrefaei. "Solving hybrid-vehicle routing problem using modified simulated annealing." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (December 1, 2021): 4922. http://dx.doi.org/10.11591/ijece.v11i6.pp4922-4931.

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<span lang="EN-US">In this paper, we consider the hybrid vehicle routing problem (HVRP) at which the vehicle consumes two types of power: fuel and electricity. The aim of this problem is to minimize the total cost of travelling between customers, provided that each customer is visited only once. The vehicle departs from the depot and returns after completing the whole route. This optimization problem is solved using a modified simulated annealing (SA) heuristic procedure with constant temperature. This approach is implemented on a numerical example and the results are compared with the SA algorithm with decreasing temperature. The obtained results show that using the SA with constant temperature overrides the SA with decreasing temperature. The results indicate that SA with decreasing temperature needs twice the number of iterations needed by the SA with constant temperature to reach a near optimum solution.</span>

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Afifah, Eka Nur, Alamsyah Alamsyah, and Endang Sugiharti. "Scheduling Optimization of Sugarcane Harvest Using Simulated Annealing Algorithm." Scientific Journal of Informatics 5, no. 2 (November 29, 2018): 138–47. http://dx.doi.org/10.15294/sji.v5i2.14421.

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Scheduling is one of the important part in production planning process. One of the factor that influence the smooth production process is raw material supply. Sugarcane supply as the main raw material in the making of sugar is the most important componen. The algorithm that used in this study was Simulated Annealing (SA) algorithm. SA apability to accept the bad or no better solution within certain time distinguist it from another local search algorithm. Aim of this study was to implement the SA algorithm in scheduling the sugarcane harvest process so that the amount of sugarcane harvest not so differ from mill capacity of the factory. Data used in this study were 60 data from sugarcane farms that ready to cut and mill capacity 1660 tons. Sugarcane harvest process in 19 days producing 33043,76 tons used SA algorithm and 27089,47 tons from factory actual result. Based on few experiments, obtained sugarcane harvest average by SA algorithm was 1651,63 tons per day and factory actual result was 1354,47 tons. Result of harvest scheduling used SA algorithm showed not so differ average from mill capacity of factory. Truck uses scheduling by SA algorithm showed average 119 trucks per day while from factory actual result was 156 trucks. With the same harvest time, SA algorithm result was greater and the amount of used truck less than actual result of factory. Thus, can be concluded SA algorithm can make the scheduling of sugarcane harvest become more optimall compared to other methods applied by the factory nowdays.

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Furukawa, M., and P. J. Morrison. "Linear stability analysis via simulated annealing and accelerated relaxation." Physics of Plasmas 29, no. 10 (October 2022): 102504. http://dx.doi.org/10.1063/5.0101095.

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Simulated annealing (SA) is a kind of relaxation method for finding equilibria of Hamiltonian systems. A set of evolution equations is solved with SA, which is derived from the original Hamiltonian system so that the energy of the system changes monotonically while preserving Casimir invariants inherent to noncanonical Hamiltonian systems. The energy extremum reached by SA is an equilibrium. Since SA searches for an energy extremum, it can also be used for stability analysis when initiated from a state where a perturbation is added to an equilibrium. The procedure of the stability analysis is explained, and some examples are shown. Because the time evolution is computationally time consuming, efficient relaxation is necessary for SA to be practically useful. An acceleration method is developed by introducing time dependence in the symmetric kernel used in the double bracket, which is part of the SA formulation described here. An explicit formulation for low-beta reduced magnetohydrodynamics (MHD) in cylindrical geometry is presented. Since SA for low-beta reduced MHD has two advection fields that relax, it is important to balance the orders of magnitude of these advection fields.

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Redi, Anak Agung Ngurah Perwira, Fiki Rohmatul Maula, Fairuz Kumari, Natasha Utami Syaveyenda, Nanda Ruswandi, Annisa Uswatun Khasanah, and Adji Chandra Kurniawan. "Simulated annealing algorithm for solving the capacitated vehicle routing problem: a case study of pharmaceutical distribution." Jurnal Sistem dan Manajemen Industri 4, no. 1 (July 28, 2020): 41–49. http://dx.doi.org/10.30656/jsmi.v4i1.2215.

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This study aims to find a set of vehicles routes with the minimum total transportation time for pharmaceutical distribution at PT. XYZ in West Jakarta. The problem is modeled as the capacitated vehicle routing problem (CVRP). The CVRP is known as an NP-Hard problem. Therefore, a simulated annealing (SA) heuristic is proposed. First, the proposed SA performance is compared with the performance of the algorithm form previous studies to solve CVRP. It is shown that the proposed SA is useful in solving CVRP benchmark instances. Then, the SA algorithm is compared to a commonly used heuristic known as the nearest neighborhood heuristics for the case study dataset. The results show that the simulated Annealing and the nearest neighbor algorithm is performing well based on the percentage differences between each algorithm with the optimal solution are 0.03% and 5.50%, respectively. Thus, the simulated annealing algorithm provides a better result compared to the nearest neighbour algorithm. Furthermore, the proposed simulated annealing algorithm can find the solution as same as the exact method quite consistently. This study has shown that the simulated annealing algorithm provides an excellent solution quality for the problem.

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Liu, Jung-Ping, and Chen-Ming Tsai. "Binary Computer-Generated Holograms by Simulated-Annealing Binary Search." Photonics 9, no. 8 (August 18, 2022): 581. http://dx.doi.org/10.3390/photonics9080581.

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The binary computer-generated hologram (BCGH) has attracted much attention recently because it can address the high-speed binary spatial light modulator (SLM), such as a digital micromirror device (DMD) SLM. In this paper, our concern is the development of an algorithm to produce high-quality BCGHs. In particular, simulated annealing (SA) is an efficient algorithm used to produce a phase-only computer-generated hologram. In the study of SA for the production of a BCGH, we found some inherent shortcomings of SA, and the quality of the produced BCGHs is limited. Accordingly, we have modified SA and propose the simulated-annealing binary search (SABS) algorithm. We have also proposed a method to quickly determine the parameters for SABS. In the comparison with SA, the mean square error of the SABS BCGHs decreases by 32% on average. Therefore, the SABS is a promising technique for a high-quality holographic display by DMD.

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Iryanto, I., and Putu Harry Gunawan. "Numerical Approach of Symmetric Traveling Salesman Problem Using Simulated Annealing." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 6 (December 30, 2021): 1090–98. http://dx.doi.org/10.29207/resti.v5i6.3549.

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The aim of this paper is to elaborate the performance of Simulated Annealing (SA) algorithm for solving traveling salesmen problems. In this paper, SA algorithm is modified by using the interaction between outer and inner loop of algorithm. This algorithm produces low standard deviation and fast computational time compared with benchmark algorithms from several research papers. Here SA uses a certain probability as indicator for finding the best and worse solution. Moreover, the strategy of SA as cooling to temperature ratio is still given. Thirteen benchmark cases and thirteen square grid symmetric TSP are used to see the performance of the SA algorithm. It is shown that the SA algorithm has promising results in finding the best solution of the benchmark cases and the squared grid TSP with relative error 0 - 7.06% and 0 – 3.31%, respectively. Further, the SA algorithm also has good performance compared with the well-known metaheuristic algorithms in references.

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Zahran, E. G., A. A. Arafa, H. I. Saleh, and M. I. Dessouky. "Effective Hybridization of Biogeography Based Optimization and Simulated Annealing." Journal of Physics: Conference Series 2304, no. 1 (August 1, 2022): 012013. http://dx.doi.org/10.1088/1742-6596/2304/1/012013.

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Abstract Since the optimization process constitutes a great step in solving complex real world problems, the development of novel optimization algorithms is one of the growing interest topics that attracted researchers in the recent decades. This paper presents the hybridization of bio-inspired Biogeography Based Optimization (BBO) algorithm and physics-inspired Simulated Annealing (SA) algorithm, into a new variant called BBO-SA. The proposed algorithm uses the concepts of the SA to enhance the diversity of BBO solutions which in turn improves the obtained solution. For validating the performance of BBO-SA, it is compared to that of BBO algorithm in solving a set of thirteen complex benchmark functions. Validation results prove the superior performance of the proposed BBO-SA algorithm over the BBO algorithm in solving complex function in terms of escaping from local optima and reaching near optimal solution in lower execution times. Besides, the proposed algorithm is applied to solve a very challenging problem denoted as the RFID Reader Deployment Problem (RRDP). Such problem can be solved by finding the optimal distribution of the RFID readers which fulfils the set of RFID planning objectives. A comparison is held between the BBO-SA algorithm and other optimization algorithms on a large RFID model. Simulation results verified the superiority of the algorithm over the compared ones for solving the RRDP with satisfying the deployment objectives.

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Pan, Jun, Zhi Guo Cheng, and Ji Yao Lv. "Study on the TSP Problem Based on SA Algorithm." Applied Mechanics and Materials 687-691 (November 2014): 1316–19. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1316.

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This paper describes the simulated annealing algorithm and TSP problems, analyze the applicability of simulated annealing algorithm to solve TSP problem, and takes China urban travel questions as an examples to vertified the validity of the model, the results showed that when the number of iterations reached at 4000,it will obtain the optimal solution.

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Jiang, Qingye, Guojie Song, Cong Gao, Yu Wang, Wenjun Si, and Kunqing Xie. "Simulated Annealing Based Influence Maximization in Social Networks." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 127–32. http://dx.doi.org/10.1609/aaai.v25i1.7838.

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The problem of influence maximization, i.e., mining top-k influential nodes from a social network such that the spread of influence in the network is maximized, is NP-hard. Most of the existing algorithms for the prob- lem are based on greedy algorithm. Although greedy algorithm can achieve a good approximation, it is computational expensive. In this paper, we propose a totally different approach based on Simulated Annealing(SA) for the influence maximization problem. This is the first SA based algorithm for the problem. Additionally, we propose two heuristic methods to accelerate the con- vergence process of SA, and a new method of comput- ing influence to speed up the proposed algorithm. Experimental results on four real networks show that the proposed algorithms run faster than the state-of-the-art greedy algorithm by 2-3 orders of magnitude while being able to improve the accuracy of greedy algorithm.

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Li, Shu Fei. "An Improved Simulated Annealing Algorithm Based on Genetic Algorithm." Advanced Materials Research 490-495 (March 2012): 267–71. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.267.

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An effective hybrid Simulated Annealing Algorithm based on Genetic Algorithm is proposed to apply to reservoir operation. Compared with other optimal methods, it is proved that SA-GA algorithm is a quite effective optimization method to solve reservoir operation problem. The simulated annealing algorithm is introduced to Genetic Algorithm, which is feasibility and validity. As a result of stronger ability of global search and better convergence property of SA-GA, and compared with other algorithms, the approximate global optimal solution would be obtained in little time. The operation speed is more quickness and the results are more stabilization by SA-GA, than Genetic Algorithm and the traditional Dynamic Programming and POA.

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Harada, Ryuhei, Tomotake Nakamura, and Yasuteru Shigeta. "A Fast Convergent Simulated Annealing Algorithm for Protein-Folding: Simulated Annealing Outlier FLOODing (SA-OFLOOD) Method." Bulletin of the Chemical Society of Japan 89, no. 11 (November 15, 2016): 1361–67. http://dx.doi.org/10.1246/bcsj.20160244.

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Strimbu, Bogdan M., and Mihaela Paun. "Sensitivity of forest plan value to parameters of simulated annealing." Canadian Journal of Forest Research 43, no. 1 (January 2013): 28–38. http://dx.doi.org/10.1139/cjfr-2012-0277.

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Simulated annealing (SA) is a heuristic technique popular in forest planning, providing solutions close to optimality in reduced computation time. The present study challenges the common approach used to establish the parameters of SA that mimic physical processes by proving that slow cooling or large initial temperatures do not necessarily lead to optimal solutions. The study has two objectives: (1) to identify the parameters (i.e., initial temperature and annealing rate) that could supply close to optimal results with reduced experimentation time and (2) to assess the impact of parameters determining SA performances. Using three forest inventory data sets from British Columbia, we investigated the influence of initial temperature, annealing rate, and numbers of runs on forest planning solutions using a replicated completely randomized design organized as a factorial experiment within a repeated-measures framework. The optimal solution seems to be little influenced by the number of runs; our findings indicate that the combination of initial temperature and rate of annealing is critical in obtaining superior results. Furthermore, the selection of the SA parameters seems to be dependent on the harvest age, which indicates that the parameters should be selected considering whether or not a stand is harvested more than once during the planning period.

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Wang, Ruo, Changchun Yin, Miaoyue Wang, and Guangjie Wang. "Simulated annealing for controlled-source audio-frequency magnetotelluric data inversion." GEOPHYSICS 77, no. 2 (March 2012): E127—E133. http://dx.doi.org/10.1190/geo2011-0106.1.

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Simulated annealing (SA) is used to invert 1D controlled source audio-frequency magnetotelluric (CSAMT) data. In the annealing process, the system energy is taken as the root-mean-square fitting error between model responses and real data. The model parameters are the natural logarithms of the resistivity and the thickness in each layer of the earth. The annealing temperature decreases exponentially, while the model is refreshed randomly according to the temperature and is accepted according to a Boltzmann probability. We first tested the SA on synthetic data and developed a cooling schedule of model updates specifically for CSAMT data inversion. The redesigned cooling schedule reduces the magnitude of the model updating, and makes the solution converge rapidly and stably. For a three-layer model whose resistivity increases with depth, SA has difficulty in obtaining the global solution for the middle layer. However, the solution for such a layer can be significantly improved by using the mean value of the estimates. The inversion of field data from a northern suburb of Beijing, China, demonstrates that starting from a 1D smooth inversion to determine the range of SA parameters permits the SA to obtain very good results from the CSAMT survey data.

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ISHIBUCHI, Hisao, Shinta MISAKI, and Hideo TANAKA. "Simulated Annealing for Fuzzy Flow Shop Scheduling." Journal of Japan Society for Fuzzy Theory and Systems 5, no. 3 (1993): 600–615. http://dx.doi.org/10.3156/jfuzzy.5.3_600.

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Motair, Hafed M. "Hybridization Simulated Annealing Algorithm in a Single Machine Scheduling Problem." WSEAS TRANSACTIONS ON MATHEMATICS 20 (November 12, 2021): 598–606. http://dx.doi.org/10.37394/23206.2021.20.63.

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In this paper, we investigate a single machine scheduling problem (SMSP). We try to reach the optimal or near optimal solution which minimize the sum of three objective functions: total completion times, total tardiness and total earliness. Firstly, we solve this problem by Branch and bound algorithm (BAB alg) to find optimal solutions, dominance rules (DR)s are used to improve the performance of BAB alg, the resulting is BABDR, secondly, we solve this problem by simulated annealing algorithm (SA alg) as metaheuristic algorithm (MET alg). It is known that combining MET alg with other algorithms can improve the resulting solutions. In this paper we developed the concept of insertion preselected jobs one by one through all positions of remaining jobs of considered sequence, the proposed MET alg called Insertion Metaheuristic Algorithm (IMA). This procedure improves the performance of SA alg in two directions: in the first one, we use the IMA to generate initial solution for SA alg, in the second one, we use the IMA to improve the solution obtained through the iterations of SA alg. The experiments showed that IMA can improve the performance of SA alg in these two directions.

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Lalaoui, Mohamed, Abdellatif El Afia, and Raddouane Chiheb. "A Self-Tuned Simulated Annealing Algorithm Using Hidden Markov Model." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 1 (February 1, 2018): 291. http://dx.doi.org/10.11591/ijece.v8i1.pp291-298.

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Simulated Annealing algorithm (SA) is a well-known probabilistic heuristic. It mimics the annealing process in metallurgy to approximate the global minimum of an optimization problem. The SA has many parameters which need to be tuned manually when applied to a specific problem. The tuning may be difficult and time-consuming. This paper aims to overcome this difficulty by using a self-tuning approach based on a machine learning algorithm called Hidden Markov Model (HMM). The main idea is allowing the SA to adapt his own cooling law at each iteration, according to the search history. An experiment was performed on many benchmark functions to show the efficiency of this approach compared to the classical one.

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Li, Jun, and Hai Bo Pu. "A Recognition Method Using SA Optimized SVM-DS." Applied Mechanics and Materials 182-183 (June 2012): 798–804. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.798.

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Through properly setting the simulated annealing options of acceptance function, annealing function and temperature function, an adaptive hyper-parameter estimation method using simulated annealing algorithm is applied to improve the accuracy and efficiency of SVM. While, in order to eliminate the effects of error accumulation in multi-SVM, D-S theory is employed for decision fusion of SVM classifiers. When delimiting the belief and plausibility measures, recognition capability of SVM classifiers has been taken into account. And the Dempster decision rule also has been considered to the recognition result of each SVM classifier in the fusion algorithm. Finely, with the data set in the database of Statlog for the study, the experiment result indicates that this method can significantly increase the classification accuracy and demonstrate a good performance of robust.

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Liu, Jun, Bao Shou Sun, and Guo Fu Li. "Optimization of Warpage in Injection Molding Based on Simulated Annealing Algorithm." Advanced Materials Research 129-131 (August 2010): 931–35. http://dx.doi.org/10.4028/www.scientific.net/amr.129-131.931.

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To make a simulated annealing algorithm(SA) to optimize the method of warping deformation of the purpose is to enable the optimal value of the warpage. Using ActiveX technology and VB programming tools will be SA and Moldflow combination. The optimal injection molding process parameters, such as mold temperature, melt temperature, gate location were determined by according to Metropolis criterion and following a route of Monte Carlo(MC) heuristic random search determined by SA. The results of the typical model show that the wargae is accurate and reliable, and the optimization efficiency is effectively improved by applying the SA method, therefore the combination of Moldflow and SA proposed in this paper is useful for the optimization of injection molding process parameters.

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Liñán-García, Ernesto, and Lorena Marcela Gallegos-Araiza. "Simulated Annealing with Previous Solutions Applied to DNA Sequence Alignment." ISRN Artificial Intelligence 2012 (October 14, 2012): 1–6. http://dx.doi.org/10.5402/2012/178658.

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A new algorithm for solving sequence alignment problem is proposed, which is named SAPS (Simulated Annealing with Previous Solutions). This algorithm is based on the classical Simulated Annealing (SA). SAPS is implemented in order to obtain results of pair and multiple sequence alignment. SA is a simulation of heating and cooling of a metal to solve an optimization problem. In order to select randomly a current solution, SAPS algorithm chooses a solution from solutions that have been previously generated within the Metropolis Cycle. This simple change has led to increase the quality of the solution to the problem of aligning genomic sequences with respect to the classical Simulated Annealing algorithm. The parameters of SAPS, for certain instances, are tuned by an analytical method, and some parameters have experimentally been tuned. SAPS has generated high-quality results in comparison with the classical SA. The instances used are specific genes of the AIDS virus.

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Santo, Rafael Do Espírito, Fabio Henrique Pereira, and Edson Amaro Júnior. "Image Compression Based on Generalized Principal Components Analysis and Simulated Annealing." International Journal of Cognitive Informatics and Natural Intelligence 6, no. 2 (April 2012): 41–67. http://dx.doi.org/10.4018/jcini.2012040103.

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The authors propose a new data dimensionality reduction method that is formulated as an optimization problem solved in two stages. In the first stage, Generalized Principal Component Analysis (GPCA) is used to find a solution with local maximum (local solution) whereas the algorithm Simulated Annealing (SA) is performed, in the second stage, to converge the local solution to the optimal solution. The performance of GPCA and GPCA with Simulated Annealing (GPCA-SA) as images compressors was evaluated in terms of the Compression Peak Signal-to-Noise Rate (CPSNR), memory size necessary to store the resulting compressed image and Contrast-to-Noise ratio. The results show that GPCA and GPCA-SA requires the same amount of memory to store compressed data, but GPCA-SA provides better CPSNR than GPCA. They also compared the performance of our designed method with a wavelet-based compression technique widely used in medical imaging, known as Lifting, to demonstrate the efficiency of GPCA-SA in clinical application.

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AlHadid, Issam, Khalid Kaabneh, and Hassan Tarawneh. "Hybrid Simulated Annealing with Meta-Heuristic Methods to Solve UCT Problem." Modern Applied Science 12, no. 11 (October 29, 2018): 366. http://dx.doi.org/10.5539/mas.v12n11p366.

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Simulated Annealing (SA) is a common meta-heuristic algorithm that has been widely used to solve complex optimization problems. This work proposes a hybrid SA with EMC to divert the search effectively to another promising region. Moreover, a Tabu list memory applied to avoid cycling. Experimental results showed that the solution quality has enhanced using SA-EMCQ by escaping the search space from local optimum to another promising region space. In addition, the results showed that our proposed technique has outperformed the standard SA and gave comparable results to other approaches in the literature when tested on ITC2007-Track3 university course timetabling datasets.

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AlHadid, Issam, Khalid Kaabneh, and Hassan Tarawneh. "Hybrid Simulated Annealing with Meta-Heuristic Methods to Solve UCT Problem." Modern Applied Science 12, no. 11 (October 29, 2018): 385. http://dx.doi.org/10.5539/mas.v12n11p385.

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Simulated Annealing (SA) is a common meta-heuristic algorithm that has been widely used to solve complex optimization problems. This work proposes a hybrid SA with EMC to divert the search effectively to another promising region. Moreover, a Tabu list memory applied to avoid cycling. Experimental results showed that the solution quality has enhanced using SA-EMCQ by escaping the search space from local optimum to another promising region space. In addition, the results showed that our proposed technique has outperformed the standard SA and gave comparable results to other approaches in the literature when tested on ITC2007-Track3 university course timetabling datasets.

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Khalfe, Nadeem, Kumar Lahiri, and Kumar Wadhwa. "Simulated annealing technique to design minimum cost exchanger." Chemical Industry and Chemical Engineering Quarterly 17, no. 4 (2011): 409–27. http://dx.doi.org/10.2298/ciceq110204027k.

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Owing to the wide utilization of heat exchangers in industrial processes, their cost minimization is an important target for both designers and users. Traditional design approaches are based on iterative procedures which gradually change the design and geometric parameters to satisfy a given heat duty and constraints. Although well proven, this kind of approach is time consuming and may not lead to cost effective design as no cost criteria are explicitly accounted for. The present study explores the use of nontraditional optimization technique: called simulated annealing (SA), for design optimization of shell and tube heat exchangers from economic point of view. The optimization procedure involves the selection of the major geometric parameters such as tube diameters, tube length, baffle spacing, number of tube passes, tube layout, type of head, baffle cut etc and minimization of total annual cost is considered as design target. The presented simulated annealing technique is simple in concept, few in parameters and easy for implementations. Furthermore, the SA algorithm explores the good quality solutions quickly, giving the designer more degrees of freedom in the final choice with respect to traditional methods. The methodology takes into account the geometric and operational constraints typically recommended by design codes. Three different case studies are presented to demonstrate the effectiveness and accuracy of proposed algorithm. The SA approach is able to reduce the total cost of heat exchanger as compare to cost obtained by previously reported GA approach.

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Sun, Bo, Jian Cang Xie, and Ni Wang. "Application of Urban Water Demand Prediction Model by Using Particle Swarm Algorithm Based on Simulated Annealing." Applied Mechanics and Materials 155-156 (February 2012): 102–6. http://dx.doi.org/10.4028/www.scientific.net/amm.155-156.102.

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Water demand prediction is a complicated multifactor, multi-level non-linear system influenced by the urban population, industrial and economic level. The results of the prediction accuracy have a greater uncertainty and ambiguity. As a new cluster intelligent evolutionary algorithms, particle swarm optimization (PSO) is easy to understand, easy to implement ,and it is very suitable for non-linear model parameters fitting problems. At the same time, we will introduce the simulated annealing mechanism into particle swarm optimization algorithm, constructed the optimization algorithm of simulated annealing particle swarm (SA-PSO). In the paper, the optimization algorithm of simulated annealing particle swarm (SA-PSO) is applied to the field of water demand prediction. Example show that compared with the particle swarm algorithm, simulated annealing particle swarm optimization achieves a high prediction accuracy for urban water demand prediction, and it is strong applicability in the water demand forecast.

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Chen, Ruey Maw, and Frode Eika Sandnes. "A Novel Non-Decreasing Temperature Based Simulated Annealing for Flow Shop Problems." Applied Mechanics and Materials 764-765 (May 2015): 1390–94. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.1390.

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The permutation flow shop problem (PFSP) is an NP-hard permutation sequencing scheduling problem, many meta-heuristics based schemes have been proposed for finding near optimal solutions. A simple insertion simulated annealing (SISA) scheme consisting of two phases is proposed for solving PFSP. First, to reduce the complexity, a simple insertion local search is conducted for constructing the solution. Second, to ensure continuous exploration in the search space, two non-decreasing temperature control mechanisms named Heating SA and Steady SA are introduced in a simulated annealing (SA) procedure. The Heating SA increases the exploration search ability and the Steady SA enhances the exploitation search ability. The most important feature of SISA is its simple implementation and low computation time complexity. Experimental results are compared with other state-of-the-art algorithms and reveal that SISA is able to efficiently yield good permutation schedule.

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Fendji, Jean Louis Kedieng Ebongue, and Chris Thron. "A Simulated Annealing Based Centre of Mass (SAC) Approach for Mesh Routers Placement in Rural Areas." International Journal of Operations Research and Information Systems 11, no. 1 (January 2020): 37–65. http://dx.doi.org/10.4018/ijoris.2020010102.

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The problem of node placement in a rural wireless mesh network (RWMN) consists of determining router placement which minimizes the number of routers while providing good coverage of the area of interest. This problem is NP-hard with a factorial complexity. This article introduces a new approach, called the simulated annealing-based centre of mass (SAC) for solving this placement problem. The intent of this approach is to improve the robustness and the quality of solution, and to minimize the convergence time of a simulated annealing (SA) approach in solving the same problem in small and large scale. SAC is compared to the centre of mass (CM) and simulated annealing (SA) approaches. The performances of these algorithms were evaluated on a set of 24 instances. The experimental results show that the SAC approach provides the best robustness and solution quality, while decreasing by half the convergence time of the SA algorithm.

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Ren-Yin, Song, and Liang Guo-Xiang. "Research on AC Asynchronous Motor Vector Control Speed Control System Based on Labview." Open Electrical & Electronic Engineering Journal 9, no. 1 (November 4, 2015): 547–52. http://dx.doi.org/10.2174/1874129001509010547.

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During the process of using traditional particle swarm optimization (PSO) to identify electric parameters, the algorithm can be easily caught in locally optimal solution, thus leading to relatively large identification result error. Therefore, the article proposes a simulated annealing particle swarm optimization (SA-PSO) algorithm to integrate the advantages, namely the strong global optimization capability of simulated annealing (SA) algorithm and the fast convergence speed of PSO algorithm, in order to improve the traditional PSO algorithm, and uses simulated annealing principle to determine inertia weight of PSO algorithm. Meanwhile, DFIG with the unit capacity of 1.5MW has been taken as the research object for simulation analysis. The test system employs LabVIEW software for experiment design, and the result shows: compared with the identification result of traditional PSO algorithm, SA-PSO algorithm can rapidly and accurately identify DFIG electric parameters and the identification result has higher precision.

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Dao, Tran Trong, Ivan Zelinka, and Vo Hoang Duy. "Use of Simulated Annealing for Adaptive Control System." International Journal of Energy Optimization and Engineering 2, no. 3 (July 2013): 42–54. http://dx.doi.org/10.4018/ijeoe.2013070103.

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This work deals with using a method of artificial intelligence, mainly the generic probabilistic meta-algorithm can be used in such a difficult task which is analyzed and control of dynamical systems. Simulated annealing (SA) is used in this investigation. The adaptive control system was used in simulations with optimization by Simulated Annealing and the results are presented in graphs.

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Cherabli, Meriem, Megdouda Ourbih-Tari, and Meriem Boubalou. "Refined descriptive sampling simulated annealing algorithm for solving the traveling salesman problem." Monte Carlo Methods and Applications 28, no. 2 (May 31, 2022): 175–88. http://dx.doi.org/10.1515/mcma-2022-2113.

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Abstract The simulated annealing (SA) algorithm is a popular intelligent optimization algorithm which has been successfully applied in many fields. In this paper, we propose a software component under the Windows environment called goRDS which implements a refined descriptive sampling (RDS) number generator of high quality in the MATLAB programming language. The aim of this generator is to sample random inputs through the RDS method to be used in the Simple SA algorithm with swap operator. In this way, the new probabilistic meta-heuristic algorithm called RDS-SA algorithm will enhance the simple SA algorithm with swap operator, the SA algorithm and possibly its variants with solutions of better quality and precision. Towards this goal, the goRDS generator was highly tested by adequate statistical tests and compared statistically to the random number generator (RNG) of MATLAB, and it was proved that goRDS has passed all tests better. Simulation experiments were carried out on the benchmark traveling salesman problem (TSP) and the results show that the solutions obtained with the RDS-SA algorithm are of better quality and precision than those of the simple SA algorithm with swap operator, since the software component goRDS represents the probability behavior of the SA input random variables better than the usual RNG.

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Yang, Min, and Ming Yan Jiang. "Hybrid Spectrum Access and Power Allocation Based on Improved Hopfield Neural Networks." Advanced Materials Research 588-589 (November 2012): 1490–94. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.1490.

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This paper aims to solve the optimization power allocation problem based on cognitive radio network system. We propose a Hybrid Spectrum Access (HSA) method which considers the total transmit power constraint, the peak power constraint and the primary users’ tolerance. In order to solve this combinational optimization problem and achieve the global optimal solution, we derived a Simulated Annealing-Hopfield neural networks (SA-HNN). The simulation results of the optimized ergodic capacity shows that the proposed optimization problem can be solved more efficiently and better by SA-HNN than HNN or Simulated Annealing (SA), and the proposed HSA method by SA-HNN can achieve a better ergodic capacity than the traditional methods.

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Dutta, Diptam. "Generic Algorithm Implementation of Approximation Algorithm using Simulated Annealing (SA)." International Journal of Computer Applications 81, no. 3 (November 15, 2013): 17–24. http://dx.doi.org/10.5120/13992-2014.

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de Abreu, N. M. M., T. M. Querido, and P. O. Boaventura-Netto. "Redinv-SA: la simulated annealing for the quadratic assignment problem." RAIRO - Operations Research 33, no. 3 (July 1999): 249–73. http://dx.doi.org/10.1051/ro:1999111.

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ZHANG, DAKUN, GUOZHI SONG, KUNLIANG LIU, YONG MA, CHENGLONG ZHAO, and XU AN WANG. "Comprehensive Improved Simulated Annealing Optimization for Floorplanning of Heterogeneous 3D Networks-on-Chip." Journal of Interconnection Networks 15, no. 03n04 (September 2015): 1540006. http://dx.doi.org/10.1142/s021926591540006x.

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With the rapid development of integrated circuit manufacturing processes, poor system scalability has become a prominent problem for System on Chip (SoC).To solve the bottleneck problems such as global synchronization, network on chip Networks on Chip (NoC) has emerged as a new design to tackle the increasing communication demand among elements on chips. With the development of networks-on-chip, the research has expanded from two-dimensional to three-dimensional design, and 3D networks-on-chip is a combination of 3D integration technology and 2D networks-on-chips with the advantages of both to meet the development trend of diversified chip functions. This paper presents an improved floorplanning optimization algorithm based on simulated annealing algorithm (Comprehensive Improved Simulated Annealing, hereinafter referred to as CISA algorithm) to replace the original floorplanning optimization algorithm based on simulated annealing algorithm (Simulated Annealing, hereinafter referred to as SA algorithm) to make it more applicable to the three-dimensional network-on- chip simulation. This paper describes the CISA algorithm improvement ideas and uses it on an existing 3D network-on-chip simulator with a set of classical simulation tests. The results show that the proposed CISA algorithm is better than the original SA algorithm and it is more suitable for simulations of three-dimensional networks-on-chip, especially when dealing with large scale 3D NoC.

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Cao, Yan, and Jiang Du. "An Improved Simulated Annealing Algorithm for Real-Time Dynamic Job-Shop Scheduling." Advanced Materials Research 186 (January 2011): 636–39. http://dx.doi.org/10.4028/www.scientific.net/amr.186.636.

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Job-shop scheduling is one of the core research aspects of Manufacturing Execution System (MES). It is significant for improving the utilization of enterprise resources, enhancing product quality, shortening delivery periods, reducing product cost, and raising enterprise competitive power in market economy. In order to solve this problem, Simulated Annealing (SA) algorithm is improved to solve large-scale combinatorial problem of job-shop scheduling. To make the SA algorithm more effective to solve job-shop scheduling problems, a solution encoding mode, scheduling scheme generation, initial temperature selection, temperature updating function, Markov chain length, end rule, and so on of the improved SA algorithm are discussed that affect the computation speed and convergence of the SA algorithm. Finally, the improved SA algorithm is validated by a job–shop scheduling problem of 10 workpieces and 10 machines.

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Yang, Xiao Hui, He Sheng Liu, and Long Long Hu. "Optimize Track Design and its Simulation Based on Simulated Annealing Algorithm." Advanced Materials Research 482-484 (February 2012): 599–602. http://dx.doi.org/10.4028/www.scientific.net/amr.482-484.599.

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In this paper, in order to optimize the shape of a snowboard course, we address the problem with the consideration of four key parameters.Based on a detailed physical analysis, we establish a model to simulate the trajectory of a skilled snowboarder by using Simulated Annealing. Finally, we adopt (SA) algorithm to find out the optimal snowboard course.

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Mohseni, Mohsen, and Mohammad Karim Sohrabi. "MVPP-Based Materialized View Selection in Data Warehouses Using Simulated Annealing." International Journal of Cooperative Information Systems 29, no. 03 (August 28, 2020): 2050001. http://dx.doi.org/10.1142/s021884302050001x.

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The process of extracting data from different heterogeneous data sources, transforming them into an integrated, unified and cleaned repository, and storing the result as a single entity leads to the construction of a data warehouse (DW), which facilitates access to data for the users of information systems and decision support systems. Due to their enormous volumes of data, processing of analytical queries of decision support systems need to scan very large amounts of data, which has a negative effect on the systems’ response time. Because of the special importance of online analytical processing (OLAP) in these systems, to enhance the performance and improve the query response time of the system, an appropriate number of views of the DW are selected for materialization and will be utilized for responding to the analytical queries, instead of direct access to the base relations. Memory constraint and views maintenance overhead are two main limitations that make it impossible, in most cases, to materialize all views of the DW. Selecting a proper set of views of DW for materialization, called materialized view selection (MVS) problem, is an important research issue that has been focused in various papers. In this paper, we have proposed a method, called P-SA, to select an appropriate set of views using an improved version of simulated annealing (SA) algorithm that utilizes a proper neighborhood selection strategy. P-SA uses the multiple view processing plan (MVPP) structure for selecting the views. Data and queries of a benchmark DW have been used in experimental results for evaluating the introduced method. The experimental results show better performance of the P-SA compared to other SA-based MVS methods for increasing the number of queries, in terms of the total cost of view maintenance and query processing. Moreover, the total cost of queries in the P-SA is also better than the other important SA-based MVS methods of the literature when the size of the DW is increased.

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Duan, Wei, Hong Zhang, and Chao Wang. "Deformation Estimation for Time Series InSAR Using Simulated Annealing Algorithm." Sensors 19, no. 1 (December 31, 2018): 115. http://dx.doi.org/10.3390/s19010115.

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Time series interferometric synthetic aperture radar SAR (TSInSAR) is one of the most important surface deformation monitoring techniques, and has been widely used in geodesy. Deformation estimation is one of the main steps of TSInSAR processing, so an effective and efficient algorithm is necessary. Present algorithms have some limitations such as computing c osts or errors caused by local extremums. In this work, a novel deformation estimation method based on the simulated annealing (SA) algorithm is proposed to handle this problem. The SA algorithm uses a random search to avoid local extremums and thus can be more likely to get the global optimal solution of deformation. By adopting a better annealing method, this algorithm gets high precision deformation results in less time than most present algorithms. In addition, it can estimate complex nonlinear deformation without adding any computing costs. The results, tested on the real SAR data, confirm the reliability and effectiveness of the SA-based deformation estimation algorithm.

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Islam, Md Shabiul, Most Tahamina Khatoon, Kazy Noor-e.-Alam Siddiquee, Wong Hin Yong, and Mohammad Nurul Huda. "Performance Analysis of Simulated Annealing and Genetic Algorithm on systems of linear equations." F1000Research 10 (December 20, 2021): 1297. http://dx.doi.org/10.12688/f1000research.73581.1.

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Problem solving and modelling in traditional substitution methods at large scale for systems using sets of simultaneous equations is time consuming. For such large scale global-optimization problem, Simulated Annealing (SA) algorithm and Genetic Algorithm (GA) as meta-heuristics for random search technique perform faster. Therefore, this study applies the SA to solve the problem of linear equations and evaluates its performances against Genetic Algorithms (GAs), a population-based search meta-heuristic, which are widely used in Travelling Salesman problems (TSP), Noise reduction and many more. This paper presents comparison between performances of the SA and GA for solving real time scientific problems. The significance of this paper is to solve the certain real time systems with a set of simultaneous linear equations containing different unknown variable samples those were simulated in Matlab using two algorithms-SA and GA. In all of the experiments, the generated random initial solution sets and the random population of solution sets were used in the SA and GA respectively. The comparison and performances of the SA and GA were evaluated for the optimization to take place for providing sets of solutions on certain systems. The SA algorithm is superior to GA on the basis of experimentation done on the sets of simultaneous equations, with a lower fitness function evaluation count in MATLAB simulation. Since, complex non-linear systems of equations have not been the primary focus of this research, in future, performances of SA and GA using such equations will be addressed. Even though GA maintained a relatively lower number of average generations than SA, SA still managed to outperform GA with a reasonably lower fitness function evaluation count. Although SA sometimes converges slowly, still it is efficient for solving problems of simultaneous equations in this case. In terms of computational complexity, SA was far more superior to GAs.

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Weber, T. O., and Wilhelmus A. M. V. Noije. "Multi-Objective Design of Analog Integrated Circuits Using Simulated Annealing with Crossover Operator and Weight Adjusting." Journal of Integrated Circuits and Systems 7, no. 1 (December 27, 2012): 7–15. http://dx.doi.org/10.29292/jics.v7i1.351.

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This paper approaches the problem of analog circuit synthesis through the use of a Simulated Annealing algorithm with capability of performing crossovers with past anchor solutions (solutions better than all the others in one of the specifications) and modifying the weight of the Aggregate Objective Function specifications in order to escape local minimums. Search for the global optimum is followed by search for the Pareto front, which represents the trade-offs involved in the design and it is performed using the proposed algorithm together with Particle Swarm Optimization. In order to check the performance of the algorithm, the synthesis of a Miller Amplifier was accomplished in two different situations. The first was the comparison of 40 syntheses for Adaptive Simulated Annealing (ASA), Simulate Annealing/Quenching (SA/SQ) and the proposed SA/SQ algorithm with crossovers using a 20-minute bounded optimization with the aim of comparing the solutions of each method. Results were compared using Wilcoxon-Mann-Whitney test with a significance of 0.05 and showed that simulated annealing with crossovers have higher change of returning a good solution than the other algorithms used in this test. The second situation was the synthesis not bounded by time aiming to achieve the best circuit in order to test the use of crossovers in SA/SQ. The final amplifier using the proposed algorithm had 15.6 MHz of UGF, 82.6 dBV, 61º phase margin, 26 MV/s slew rate, area of 980 μm² and current supply of 297 μA in a 0.35 μm technology and was performed in 84 minutes.

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Naskar, Pulak, Srijeeta Talukder, and Pinaki Chaudhury. "An adaptive mutation simulated annealing based investigation of Coulombic explosion and identification of dissociation patterns in (CO2)n2+ clusters." Physical Chemistry Chemical Physics 19, no. 14 (2017): 9654–68. http://dx.doi.org/10.1039/c7cp00655a.

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In this communication, we would like to discuss the advantages of adaptive mutation simulated annealing (AMSA) over standard simulated annealing (SA) in studying the Coulombic explosion of (CO₂)_n²⁺ clusters for n = 20–68, where ‘n’ is the size of the cluster.

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Lan, Tian-Syung, and Min-Chie Chiu. "OPTIMAL NOISE CONTROL ON PLANT USING SIMULATED ANNEALING." Transactions of the Canadian Society for Mechanical Engineering 32, no. 3-4 (September 2008): 423–38. http://dx.doi.org/10.1139/tcsme-2008-0028.

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Noise control is important and essential in a manufacturing factory, where the noise level is restricted by the Occupational Safety and Health Act. Several researches on new techniques of single noise control have been well addressed and developed; however, the study of noise depression on the whole plant noise by using optimum allocation planning is hardly found. An improper machine allocation will not only result in the tremendous cost on noise control task, but also cause the harmful environment for the neighborhood; therefore, the approach of optimum and economic allocation of noise sources within a constrained plant area becomes crucial and obligatory. In this paper, a novel technique of simulated annealing (SA) is applied in the numerical optimization, and the multi-noise plant with various sound monitoring systems is also introduced. Before optimization, the single noise is tested and compared with the simulated data from SoundPlan, a commercial sound simulation package, for the accuracy check of the mathematical model. The result reveals to be within good agreements. The proposed SA optimization on the allocation of multi-noise plant surely provides an economic and effective methodology in reducing the sound accumulation around the plant boundary.

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Journal articles on the topic 'Simulated Annealing (SA)'

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