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Journal articles on the topic 'Backtracking search optimization algorithm'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Sikelis, Konstantinos, and George E. Tsekouras. "Feature Selection with a Backtracking Search Optimization Algorithm." ITM Web of Conferences 43 (2022): 01018. http://dx.doi.org/10.1051/itmconf/20224301018.

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Feature selection carries significance in the outcome of any classification or regression task. Exercising evolutionary computation algorithms in feature selection has led to the construction of efficient discrete optimization algorithms. In this paper, a modified backtracking search algorithm is employed to perform wrapper-based feature selection, where two modifications of the standard backtracking search algorithm are adopted. The first one concentrates on utilizing a particle ranking operator regarding the current population. The second one focuses on removing the case of using a single pa

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2

Xu, Qiu Yan. "Backtracking Search Optimization Algorithm with Low-Discrepancy Sequences for Mechanical Design Optimization Problems." Applied Mechanics and Materials 635-637 (September 2014): 270–73. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.270.

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This paper presents the backtracking search optimization algorithm with low-discrepancy sequences to solve mechanical design optimization problems involving problem-specific constraints and many different variables. Similar to other evolutionary algorithms, backtracking search optimization algorithm is sensitive to the initial population. Generally speaking, since there is no information about the optimization problem, the initial population should be created uniformly. The low-discrepancy sequences are employed to increase the uniformity of the initial population. The benchmark problems widel

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3

Ghanem, Khadoudja, and Abdesslem Layeb. "Feature Selection and Knapsack Problem Resolution Based on a Discrete Backtracking Optimization Algorithm." International Journal of Applied Evolutionary Computation 12, no. 2 (2021): 1–15. http://dx.doi.org/10.4018/ijaec.2021040101.

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Backtracking search optimization algorithm is a recent stochastic-based global search algorithm for solving real-valued numerical optimization problems. In this paper, a binary version of backtracking algorithm is proposed to deal with 0-1 optimization problems such as feature selection and knapsack problems. Feature selection is the process of selecting a subset of relevant features for use in model construction. Irrelevant features can negatively impact model performances. On the other hand, knapsack problem is a well-known optimization problem used to assess discrete algorithms. The objecti

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4

Civicioglu, Pinar. "Backtracking Search Optimization Algorithm for numerical optimization problems." Applied Mathematics and Computation 219, no. 15 (2013): 8121–44. http://dx.doi.org/10.1016/j.amc.2013.02.017.

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5

Wang, Shu, Xinyu Da, Mudong Li, and Tong Han. "Adaptive backtracking search optimization algorithm with pattern search for numerical optimization." Journal of Systems Engineering and Electronics 27, no. 2 (2016): 395–406. http://dx.doi.org/10.1109/jsee.2016.00041.

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6

Duan, Haibin, and Qinan Luo. "Adaptive Backtracking Search Algorithm for Induction Magnetometer Optimization." IEEE Transactions on Magnetics 50, no. 12 (2014): 1–6. http://dx.doi.org/10.1109/tmag.2014.2342192.

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7

Wei, Fengtao, Yunpeng Shi, Junyu Li, and Yangyang Zhang. "Multi-strategy synergy-based backtracking search optimization algorithm." Soft Computing 24, no. 19 (2020): 14305–26. http://dx.doi.org/10.1007/s00500-020-05225-8.

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8

Chen, Debao, Feng Zou, Renquan Lu, and Suwen Li. "Backtracking search optimization algorithm based on knowledge learning." Information Sciences 473 (January 2019): 202–26. http://dx.doi.org/10.1016/j.ins.2018.09.039.

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9

Guney, Kerim, and Ali Durmus. "Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm." International Journal of Antennas and Propagation 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/713080.

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An evolutionary method based on backtracking search optimization algorithm (BSA) is proposed for linear antenna array pattern synthesis with prescribed nulls at interference directions. Pattern nulling is obtained by controlling only the amplitude, position, and phase of the antenna array elements. BSA is an innovative metaheuristic technique based on an iterative process. Various numerical examples of linear array patterns with the prescribed single, multiple, and wide nulls are given to illustrate the performance and flexibility of BSA. The results obtained by BSA are compared with the resul

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Pradhan, Moumita, Provas Kumar Roy, and Tandra Pal. "Economic Load Dispatch Using Oppositional Backtracking Search Algorithm." International Journal of Energy Optimization and Engineering 6, no. 2 (2017): 79–97. http://dx.doi.org/10.4018/ijeoe.2017040105.

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In this paper, an oppositional backtracking search algorithm (OBSA) is proposed to solve the large scale economic load dispatch (ELD) problem. The main drawback of the conventional backtracking search algorithm (BSA) is that it produces a local optimal solution rather than the global optimal solution. The proposed OBSA methodology is a highly-constrained optimization problem has to minimize the total generation cost by satisfying several constraints involving load demand, generation limits, prohibited operating zone, ramp rate limits and valve point loading effect. The proposed method is appli

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Booth, Kyle E. C., Bryan O'Gorman, Jeffrey Marshall, Stuart Hadfield, and Eleanor Rieffel. "Quantum-accelerated constraint programming." Quantum 5 (September 28, 2021): 550. http://dx.doi.org/10.22331/q-2021-09-28-550.

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Constraint programming (CP) is a paradigm used to model and solve constraint satisfaction and combinatorial optimization problems. In CP, problems are modeled with constraints that describe acceptable solutions and solved with backtracking tree search augmented with logical inference. In this paper, we show how quantum algorithms can accelerate CP, at both the levels of inference and search. Leveraging existing quantum algorithms, we introduce a quantum-accelerated filtering algorithm for the alldifferent global constraint and discuss its applicability to a broader family of global constraints

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12

Dasgupta, Koustav, and Provas Kumar Roy. "Short Term Hydro-Thermal Scheduling Using Backtracking Search Algorithm." International Journal of Applied Metaheuristic Computing 11, no. 4 (2020): 38–63. http://dx.doi.org/10.4018/ijamc.2020100103.

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In this article, a new optimization technique, the backtracking search algorithm (BSA), is proposed to solve the hydrothermal scheduling problem. The BSA has mainly unique five steps: (i) Initialization; (ii) Selection – I; (iii) Mutation; (iv) Crossover; and (v) Selection – II; which have been applied to minimize fuel cost of the hydro-thermal scheduling problem. The BSA is very fast, robust, reliable optimization technique and gives an accurate, optimized result. Mutation and crossover are very effective steps of the BSA, which help to determine the better optimum value of the objective func

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13

Jihane, Kartite, and Cherkaoui Mohamed. "Improved backtracking search optimization algorithm for PV/Wind/FC system." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 1 (2020): 456–64. https://doi.org/10.12928/TELKOMNIKA.v18i1.11887.

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This paper uses a novel optimization method based on the improved backtracking search optimization algorithm (IBSA). The study is conducted for a hybrid stand-alone system composed of photovoltaic panel (PV), wind turbine generator and fuel cell electrolyzer (FC). To demonstrate the effectiveness of the IBSA, four benchmark functions are used. The result shows the better exploration and exploitation of the improved backtracking search optimization algorithm in terms of convergence and speed for system comprinsing PV panel wind, turbine generator and fuel cell. The proposed algorithm is used to

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14

Li, Zheng, Zhongbo Hu, Yongfei Miao, Zenggang Xiong, Xinlin Xu, and Canyun Dai. "Deep-Mining Backtracking Search Optimization Algorithm Guided by Collective Wisdom." Mathematical Problems in Engineering 2019 (December 26, 2019): 1–30. http://dx.doi.org/10.1155/2019/2540102.

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The backtracking search optimization algorithm (BSA) is a recently proposed evolutionary algorithm with simple structure and well global exploration capability, which has been widely used to solve optimization problems. However, the exploitation capability of the BSA is poor. This paper proposes a deep-mining backtracking search optimization algorithm guided by collective wisdom (MBSAgC) to improve its performance. The proposed algorithm develops two learning mechanisms, i.e., a novel topological opposition-based learning operator and a linear combination strategy, by deeply mining the winner-

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Thuan, Thanh Nguyen. "Optimal distribution network configuration using improved backtracking search algorithm." TELKOMNIKA Telecommunication, Computing, Electronics and Control 19, no. 1 (2021): pp. 301~309. https://doi.org/10.12928/TELKOMNIKA.v19i1.16773.

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Optimal network configuration is one of the effective approaches for power loss reduction of the distribution network. This paper shows a network reconfiguration method using improved backtracking search algorithm (IBSA). Wherein, IBSA is improved in the process of generating randomly the initialization population. The network reconfiguration method based on IBSA is used to find the optimal network configuration for the 33-node and 69-node systems. The results are compared to the original backtracking search algorithm (BSA), particle swarm optimization (PSO), firefly algorithm (FA) and previou

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16

Guney, K., A. Durmus, and S. Basbug. "Backtracking Search Optimization Algorithm for Synthesis of Concentric Circular Antenna Arrays." International Journal of Antennas and Propagation 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/250841.

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A backtracking search optimization algorithm (BSA) is proposed for the synthesis of concentric circular antenna arrays (CCAAs) with the low sidelobe levels at a fixed beamwidth. Several numerical examples of CCAA patterns with the single, multiple, and broad nulls imposed at the directions of interference are also given to illustrate the performance and flexibility of the proposed algorithm. BSA is a relatively new population based evolutionary optimization algorithm. The numerical results show that the design of CCAA using BSA provides good sidelobe levels with a fixed beamwidth. The nulling

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17

Wang, Lijin, Yiwen Zhong, Yilong Yin, Wenting Zhao, Binqing Wang, and Yulong Xu. "A Hybrid Backtracking Search Optimization Algorithm with Differential Evolution." Mathematical Problems in Engineering 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/769245.

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The backtracking search optimization algorithm (BSA) is a new nature-inspired method which possesses a memory to take advantage of experiences gained from previous generation to guide the population to the global optimum. BSA is capable of solving multimodal problems, but it slowly converges and poorly exploits solution. The differential evolution (DE) algorithm is a robust evolutionary algorithm and has a fast convergence speed in the case of exploitive mutation strategies that utilize the information of the best solution found so far. In this paper, we propose a hybrid backtracking search op

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18

Tsai, Hsing-Chih. "Improving backtracking search algorithm with variable search strategies for continuous optimization." Applied Soft Computing 80 (July 2019): 567–78. http://dx.doi.org/10.1016/j.asoc.2019.04.032.

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19

Zou, Feng, Debao Chen, and Renquan Lu. "Hybrid Hierarchical Backtracking Search Optimization Algorithm and Its Application." Arabian Journal for Science and Engineering 43, no. 2 (2017): 993–1014. http://dx.doi.org/10.1007/s13369-017-2852-0.

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20

Hather Ibraheem Abed. "Image segmentation with a multilevel threshold using backtracking search optimization algorithm." Tikrit Journal of Pure Science 25, no. 2 (2020): 102–9. http://dx.doi.org/10.25130/tjps.v25i2.241.

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Image segmentation is an important process in image processing. Though, there are many applications are affected by the segmentation methods and algorithms, unfortunately, not one technique, but the threshold is the popular one. Threshold technique can be categorized into two ways either simple threshold which has one threshold or multi- thresholds separated which has more than two thresholds . In this paper, image segmentation is used simple threshold method which is a simple and effective technique. Therefore, to calculate the value of threshold solution which is led to increase exponentiall

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21

Wang, Hailong, Zhongbo Hu, Yuqiu Sun, Qinghua Su, and Xuewen Xia. "Modified Backtracking Search Optimization Algorithm Inspired by Simulated Annealing for Constrained Engineering Optimization Problems." Computational Intelligence and Neuroscience 2018 (2018): 1–27. http://dx.doi.org/10.1155/2018/9167414.

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The backtracking search optimization algorithm (BSA) is a population-based evolutionary algorithm for numerical optimization problems. BSA has a powerful global exploration capacity while its local exploitation capability is relatively poor. This affects the convergence speed of the algorithm. In this paper, we propose a modified BSA inspired by simulated annealing (BSAISA) to overcome the deficiency of BSA. In the BSAISA, the amplitude control factor (F) is modified based on the Metropolis criterion in simulated annealing. The redesigned F could be adaptively decreased as the number of iterat

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22

Abed, Hather Ibraheem. "Image segmentation with a multilevel threshold using backtracking search optimization algorithm." Tikrit Journal of Pure Science 25, no. 2 (2020): 102. http://dx.doi.org/10.25130/j.v25i2.964.

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Image segmentation is an important process in image processing. Though, there are many applications are affected by the segmentation methods and algorithms, unfortunately, not one technique, but the threshold is the popular one. Threshold technique can be categorized into two ways either simple threshold which has one threshold or multi- thresholds separated which has more than two thresholds . In this paper, image segmentation is used simple threshold method which is a simple and effective technique. Therefore, to calculate the value of threshold solution which is led to increase exponentiall

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23

Zhao, Lei, Zhicheng Jia, Lei Chen, and Yanju Guo. "Improved Backtracking Search Algorithm Based on Population Control Factor and Optimal Learning Strategy." Mathematical Problems in Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/3017608.

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Backtracking search algorithm (BSA) is a relatively new evolutionary algorithm, which has a good optimization performance just like other population-based algorithms. However, there is also an insufficiency in BSA regarding its convergence speed and convergence precision. For solving the problem shown in BSA, this article proposes an improved BSA named COBSA. Enlightened by particle swarm optimization (PSO) algorithm, population control factor is added to the variation equation aiming to improve the convergence speed of BSA, so as to make algorithm have a better ability of escaping the local o

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24

Wang, Peng, Detong Zhu, and Yufeng Song. "Derivative-Free Feasible Backtracking Search Methods for Nonlinear Multiobjective Optimization with Simple Boundary Constraint." Asia-Pacific Journal of Operational Research 36, no. 03 (2019): 1950012. http://dx.doi.org/10.1142/s021759591950012x.

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In this paper, a derivative-free linear feasible direction models with backtracking search technique is considered for solving nonlinear multiobjective optimization problems subject to simple boundary constraint. The algorithm is designed to build linear interpolation models for each function of problem [Formula: see text]. We build the linear programming subproblem using linear interpolation function without the second-order derivative information. The new backtracking search step size function is given in our algorithm which guarantees both the monotone descent property of each function and

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25

Elomary, Imad, Abbou Ahmed, and Idoumghar Lhassane. "Optimization Design of the BLDC Motor Using Backtracking Search Algorithm." International Review of Electrical Engineering (IREE) 16, no. 2 (2021): 167. http://dx.doi.org/10.15866/iree.v16i2.17411.

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26

Kartite, Jihane, and Mohamed Cherkaoui. "Improved backtracking search optimization algorithm for PV/Wind/FC system." TELKOMNIKA (Telecommunication Computing Electronics and Control) 18, no. 1 (2020): 456. http://dx.doi.org/10.12928/telkomnika.v18i1.11887.

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27

Zhang, Yiying. "Backtracking search algorithm with specular reflection learning for global optimization." Knowledge-Based Systems 212 (January 2021): 106546. http://dx.doi.org/10.1016/j.knosys.2020.106546.

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28

Sheoran, Yashpal, Vineet Kumar, K. P. S. Rana, Puneet Mishra, Jitendra Kumar, and Sreejith S. Nair. "Development of Backtracking Search Optimization Algorithm Toolkit in LabVIEW™." Procedia Computer Science 57 (2015): 241–48. http://dx.doi.org/10.1016/j.procs.2015.07.476.

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29

El Maani, R., B. Radi, and A. El Hami. "Multiobjective backtracking search algorithm: application to FSI." Structural and Multidisciplinary Optimization 59, no. 1 (2018): 131–51. http://dx.doi.org/10.1007/s00158-018-2056-6.

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Karataş, Osman, Celal Yaşar, Hasan Temurtaş, and Serdar Özyön. "Crayfish Optimization Algorithm." International Scientific and Vocational Studies Journal 9, no. 1 (2025): 94–117. https://doi.org/10.47897/bilmes.1666766.

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This study aims to improve the performance of the Crayfish Optimization Algorithm (COA), a swarm intelligence algorithm recently introduced in the literature, on various test functions with fixed and variable dimensions. Optimization can be defined as making a system as efficient as possible at the least cost, within certain constraints. Numerous optimization algorithms have been designed in the literature to obtain the best solutions for specific problems. The most critical aspects in solving these problems are modeling the problem correctly, determining the parameters and constraints, and se

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Stanimirovic, Predrag, Marko Miladinovic, and Snezana Djordjevic. "Multiplicative parameters in gradient descent methods." Filomat 23, no. 3 (2009): 23–36. http://dx.doi.org/10.2298/fil0903023s.

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We introduced an algorithm for unconstrained optimization based on the reduction of the modified Newton method with line search into a gradient descent method. Main idea used in the algorithm construction is approximation of Hessian by a diagonal matrix. The step length calculation algorithm is based on the Taylor's development in two successive iterative points and the backtracking line search procedure.

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Deng, Yanchen, Shufeng Kong, and Bo An. "Pretrained Cost Model for Distributed Constraint Optimization Problems." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (2022): 9331–40. http://dx.doi.org/10.1609/aaai.v36i9.21164.

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Distributed Constraint Optimization Problems (DCOPs) are an important subclass of combinatorial optimization problems, where information and controls are distributed among multiple autonomous agents. Previously, Machine Learning (ML) has been largely applied to solve combinatorial optimization problems by learning effective heuristics. However, existing ML-based heuristic methods are often not generalizable to different search algorithms. Most importantly, these methods usually require full knowledge about the problems to be solved, which are not suitable for distributed settings where central

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Abdolrasol, Maher G. M., S. M. Suhail Hussain, Taha Selim Ustun, et al. "Artificial Neural Networks Based Optimization Techniques: A Review." Electronics 10, no. 21 (2021): 2689. http://dx.doi.org/10.3390/electronics10212689.

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In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more. The entire set of such techniqu

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Hu, Zhongbo, Ting Zhou, Qinghua Su, and Mianfang Liu. "A niching backtracking search algorithm with adaptive local search for multimodal multiobjective optimization." Swarm and Evolutionary Computation 69 (March 2022): 101031. http://dx.doi.org/10.1016/j.swevo.2022.101031.

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Civicioglu, P., U. H. Atasever, C. Ozkan, E. Besdok, A. E. Karkinli, and A. Kesikoglu. "Performance Comparison Of Evolutionary Algorithms For Image Clustering." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7 (September 19, 2014): 71–74. http://dx.doi.org/10.5194/isprsarchives-xl-7-71-2014.

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Evolutionary computation tools are able to process real valued numerical sets in order to extract suboptimal solution of designed problem. Data clustering algorithms have been intensively used for image segmentation in remote sensing applications. Despite of wide usage of evolutionary algorithms on data clustering, their clustering performances have been scarcely studied by using clustering validation indexes. In this paper, the recently proposed evolutionary algorithms (i.e., Artificial Bee Colony Algorithm (ABC), Gravitational Search Algorithm (GSA), Cuckoo Search Algorithm (CS), Adaptive Di

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Karataş, Osman, Celal Yaşar, Hasan Temurtaş, and Serdar Özyön. "Spider Wasp Optimization Algorithm." International Scientific and Vocational Studies Journal 9, no. 1 (2025): 42–67. https://doi.org/10.47897/bilmes.1659488.

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This study aims to improve the performance of the Spider Wasp Optimization (SWO) algorithm, a swarm intelligence algorithm recently introduced in the literature, on various test functions with fixed and variable dimensions. Optimization can be defined as making a system as efficient as possible with minimal cost within certain constraints. Numerous optimization algorithms have been designed in the literature and used to obtain the best solutions for specific problems. The most critical aspects in solving these problems include correctly modeling the problem, determining the problem’s parameter

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Mohd Zain, Mohamad Zihin bin, Jeevan Kanesan, Graham Kendall, and Joon Huang Chuah. "Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm." Expert Systems with Applications 91 (January 2018): 286–97. http://dx.doi.org/10.1016/j.eswa.2017.07.034.

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Lin, Jian. "Oppositional backtracking search optimization algorithm for parameter identification of hyperchaotic systems." Nonlinear Dynamics 80, no. 1-2 (2014): 209–19. http://dx.doi.org/10.1007/s11071-014-1861-8.

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Zhang, Chu, Chaoshun Li, Tian Peng, et al. "Modeling and Synchronous Optimization of Pump Turbine Governing System Using Sparse Robust Least Squares Support Vector Machine and Hybrid Backtracking Search Algorithm." Energies 11, no. 11 (2018): 3108. http://dx.doi.org/10.3390/en11113108.

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In view of the complex and changeable operating environment of pumped storage power stations and the noise and outliers in the modeling data, this study proposes a sparse robust least squares support vector machine (LSSVM) model based on the hybrid backtracking search algorithm for the model identification of a pumped turbine governing system. By introducing the maximum linearly independent set, the sparsity of the support vectors of the LSSVM model are realized, and the complexity is reduced. The robustness of the identification model to noise and outliers is enhanced using the weighted funct

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Bhattacharjee, Kuntal. "Economic Dispatch Problems Using Backtracking Search Optimization." International Journal of Energy Optimization and Engineering 7, no. 2 (2018): 39–60. http://dx.doi.org/10.4018/ijeoe.2018040102.

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The purpose of this article is to present a backtracking search optimization technique (BSA) to determine the feasible optimum solution of the economic load dispatch (ELD) problems involving different realistic equality and inequality constraints, such as power balance, ramp rate limits, and prohibited operating zone constraints. Effects of valve-point loading, multi-fuel option of large-scale thermal plants, system transmission loss are also taken into consideration for more realistic application. Two effective operations, mutation and crossover, help BSA algorithms to find the global solutio

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Tunca, Osman, and Serdar Çarbaş. "Minimum weight design of reinforced concrete beams utilizing grey wolf and backtracking search optimization algorithms." Challenge Journal of Concrete Research Letters 13, no. 2 (2022): 72. http://dx.doi.org/10.20528/cjcrl.2022.02.003.

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In this study, optimal weight design of a reinforced concrete beam subjected to various loading conditions is investigated. The purpose of the optimization is to attain the minimum weight design of the reinforced concrete beam under distributed and two-point loads. The design problem is handled under three different design load cases. The two-point loads are affected on beam-to-beam connection nodes of reinforced concrete beams. Thus, while the magnitudes of distributed load and two-points load are remained constant, the distances between two-points loads are taken as 2m, 3m and 4m, respective

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N., N. Ahmad Nazri, N. Nik Abd Malik N., Idoumghar L., M. Abdul Latiff N., and Ali S. "Backtracking Search Optimization for Collaborative Beamforming in Wireless Sensor Networks." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 4 (2018): 1801–8. https://doi.org/10.12928/TELKOMNIKA.v16i4.9058.

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Due to energy limitation and constraint in communication capabilities, the undesirable high battery power consumption has become one of the major issues in wireless sensor network (WSN). Therefore, a collaborative beamforming (CB) method was introduced with the aim to improve the radiation beampattern in order to compensate the power consumption. A CB is a technique which can increase the sensor node gain and performance by aiming at the desired objectives through intelligent capabilities. The sensor nodes were located randomly in WSN environment. The nodes were designed to cooperate among eac

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Dogadina, Elena Petrovna, Michael Viktorovich Smirnov, Aleksey Viktorovich Osipov, and Stanislav Vadimovich Suvorov. "Evaluation of the Forms of Education of High School Students Using a Hybrid Model Based on Various Optimization Methods and a Neural Network." Informatics 8, no. 3 (2021): 46. http://dx.doi.org/10.3390/informatics8030046.

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This article deals with the multicriteria programming model to optimize the time of completing home assignments by school students in both in-class and online forms of teaching. To develop a solution, we defined 12 criteria influencing the school exercises’ effectiveness. In this amount, five criteria describe exercises themselves and seven others the conditions at which the exercises are completed. We used these criteria to design a neural network, which output influences target function and the search for optimal values with three optimization techniques: backtracking search optimization alg

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Xing, Ying, Yun-Zhan Gong, Ya-Wen Wang, and Xu-Zhou Zhang. "Path-Wise Test Data Generation Based on Heuristic Look-Ahead Methods." Mathematical Problems in Engineering 2014 (2014): 1–19. http://dx.doi.org/10.1155/2014/642630.

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Path-wise test data generation is generally considered an important problem in the automation of software testing. In essence, it is a constraint optimization problem, which is often solved by search methods such as backtracking algorithms. In this paper, the backtracking algorithm branch and bound and state space search in artificial intelligence are introduced to tackle the problem of path-wise test data generation. The former is utilized to explore the space of potential solutions and the latter is adopted to construct the search tree dynamically. Heuristics are employed in the look-ahead s

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Alomari, Khaled, Osama Almarashdi, Ala Marashdh, and Belal Zaqaibeh. "A New Optimization on Harmony Search Algorithm for Exam Timetabling System." Journal of Information & Knowledge Management 19, no. 01 (2020): 2040009. http://dx.doi.org/10.1142/s0219649220400092.

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Preparing an optimal exam timetable in universities is challenging for head of departments, especially for colleges with multiple number of departments, courses, and students. Harmony search algorithm is used by many researchers to solve this problem but none of them could get an optimal solution. In this paper, a new algorithm which is called optimised harmony search algorithm with distributed selections is proposed by optimising the harmony search algorithm and the genetic algorithm. The new algorithm could satisfy hard, soft, and general constraints and generate an optimal exam timetable fo

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Boudali, Nourredine, Hadria Fizazi, and Meriem Abidi. "Texture Features Extraction and Backtracking Search Optimization Algorithm for Satellite Image Clustering." International Review of Aerospace Engineering (IREASE) 12, no. 5 (2019): 222. http://dx.doi.org/10.15866/irease.v12i5.15224.

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Debnath, Sanjib, Swapan Debbarma, Sukanta Nama, et al. "Centroid opposition-based backtracking search algorithm for global optimization and engineering problems." Advances in Engineering Software 198 (December 2024): 103784. http://dx.doi.org/10.1016/j.advengsoft.2024.103784.

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Ahandani, Morteza Alinia, Amir Rikhtehgar Ghiasi, and Hamed Kharrati. "Parameter identification of chaotic systems using a shuffled backtracking search optimization algorithm." Soft Computing 22, no. 24 (2017): 8317–39. http://dx.doi.org/10.1007/s00500-017-2779-0.

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Zhang, Chunjiang, Qun Lin, Liang Gao, and Xinyu Li. "Backtracking Search Algorithm with three constraint handling methods for constrained optimization problems." Expert Systems with Applications 42, no. 21 (2015): 7831–45. http://dx.doi.org/10.1016/j.eswa.2015.05.050.

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El-Fergany, Attia. "Optimal allocation of multi-type distributed generators using backtracking search optimization algorithm." International Journal of Electrical Power & Energy Systems 64 (January 2015): 1197–205. http://dx.doi.org/10.1016/j.ijepes.2014.09.020.

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