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Journal articles on the topic 'Algorithm Backtracking'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

Priestley, H. A., and M. P. Ward. "A Multipurpose Backtracking Algorithm." Journal of Symbolic Computation 18, no. 1 (1994): 1–40. http://dx.doi.org/10.1006/jsco.1994.1035.

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2

Van Beek, P., and D. W. Manchak. "The Design and Experimental Analysis of Algorithms for Temporal Reasoning." Journal of Artificial Intelligence Research 4 (January 1, 1996): 1–18. http://dx.doi.org/10.1613/jair.232.

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Many applications -- from planning and scheduling to problems in molecular biology -- rely heavily on a temporal reasoning component. In this paper, we discuss the design and empirical analysis of algorithms for a temporal reasoning system based on Allen's influential interval-based framework for representing temporal information. At the core of the system are algorithms for determining whether the temporal information is consistent, and, if so, finding one or more scenarios that are consistent with the temporal information. Two important algorithms for these tasks are a path consistency algor

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3

Gregorics, Tibor. "Object-oriented backtracking." Acta Universitatis Sapientiae, Informatica 9, no. 2 (2017): 144–61. http://dx.doi.org/10.1515/ausi-2017-0010.

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Abstract Several versions of the backtracking are known. In this paper, those versions are in focus which solve the problems whose problem space can be described with a special directed tree. The traversal strategies of this tree will be analyzed and they will be implemented in object-oriented style. In this way, the traversal is made by an enumerator object which iterates over all the paths (partial solutions) of the tree. Two different “acktracking enumerators” are going to be presented and the backtracking algorithm will be a linear search over one of these enumerators. Since these algorith

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4

Chen, X., and P. Van Beek. "Conflict-Directed Backjumping Revisited." Journal of Artificial Intelligence Research 14 (March 1, 2001): 53–81. http://dx.doi.org/10.1613/jair.788.

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In recent years, many improvements to backtracking algorithms for solving constraint satisfaction problems have been proposed. The techniques for improving backtracking algorithms can be conveniently classified as look-ahead schemes and look-back schemes. Unfortunately, look-ahead and look-back schemes are not entirely orthogonal as it has been observed empirically that the enhancement of look-ahead techniques is sometimes counterproductive to the effects of look-back techniques. In this paper, we focus on the relationship between the two most important look-ahead techniques---using a variable

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5

CHOWDHURY, U., and D. K. GUPTA. "MODIFIED BACKJUMPING ALGORITHMS FOR SOLVING CONSTRAINT SATISFACTION PROBLEMS." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 01 (1999): 133–47. http://dx.doi.org/10.1142/s0218001499000082.

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The backtracking algorithm is a prominent search technique in AI, particularly due to its use in Constraint Satisfaction Problems (CSPs), Truth Maintenance Systems (TMS), and PROLOG. In the context of CSPs, Dechter5 and Gashnig10 proposed two variants of the backtracking algorithm known as backjumping algorithms. One is graph-based and the other is failure-based backjumping algorithm. These algorithms attempt to backjump to the source of failure in case of a dead-end situation. This improves the backtracking performance. However, these algorithms are not consistent in the selection of the vari

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6

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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Janakiram, V. K., D. P. Agrawal, and R. Mehrotra. "A randomized parallel backtracking algorithm." IEEE Transactions on Computers 37, no. 12 (1988): 1665–76. http://dx.doi.org/10.1109/12.9745.

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8

Li, Hui, and Ping Hua Chen. "Improved Backtracking-Forward Algorithm for Maximum Matching Chinese Word Segmentation." Applied Mechanics and Materials 536-537 (April 2014): 403–6. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.403.

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In order to improve the accuracy of segmentation, analysis of backtracking-forward maximum matching algorithm exists two defects when dealing with crossing ambiguity, and on this basis, an improved-backtracking forward algorithm for maximum matching algorithm is presented. The improved algorithm is based on the backtracking-forward maximum matching algorithm and adds a module, a chain length of one and 3-words, that can detect and process crossing ambiguity, and taking advantage of counting method, we can merely sort out the defragmenter fields that occurred crossing ambiguity. A number of sel

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9

Li, Bingbing, Li Ding, Mark Rajai, Di Hu, and Shengzi Zheng. "Backtracking Algorithm-based Disassembly Sequence Planning." Procedia CIRP 69 (2018): 932–37. http://dx.doi.org/10.1016/j.procir.2017.12.007.

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10

Wang, Shijie, Guiling Sun, and Yangyang Li. "SVD++ Recommendation Algorithm Based on Backtracking." Information 11, no. 7 (2020): 369. http://dx.doi.org/10.3390/info11070369.

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Collaborative filtering (CF) has successfully achieved application in personalized recommendation systems. The singular value decomposition (SVD)++ algorithm is employed as an optimized SVD algorithm to enhance the accuracy of prediction by generating implicit feedback. However, the SVD++ algorithm is limited primarily by its low efficiency of calculation in the recommendation. To address this limitation of the algorithm, this study proposes a novel method to accelerate the computation of the SVD++ algorithm, which can help achieve more accurate recommendation results. The core of the proposed

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11

M-Noori, M., and B. Tayfeh-Rezaie. "Backtracking algorithm for finding t-designs." Journal of Combinatorial Designs 11, no. 4 (2003): 240–48. http://dx.doi.org/10.1002/jcd.10045.

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12

Jiang, Hong, and José M. Vidal. "The message management asynchronous backtracking algorithm." Journal of Experimental & Theoretical Artificial Intelligence 20, no. 2 (2008): 95–110. http://dx.doi.org/10.1080/09528130701478629.

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13

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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14

Xu, Min Li, Li Juan Yu, Cheng Ming Chen, and Shou Qi Cao. "Study on an Anti-Collision Algorithm of RFID Based on Binary Searching of Backtracking." Applied Mechanics and Materials 496-500 (January 2014): 1821–24. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.1821.

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The problem of anti-collision tag is a common problem of the radio frequency identification (RFID) system. ALOHA algorithm and binary search algorithm is the main algorithm to solve the collision problem. This paper analyzes the effectiveness of binary searching of backtracking algorithm in time slot number of shortening identification tags. Combining the theoretical analysis what needs to be improved in binary searching of backtracking algorithm.

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15

Li, Jin, Chengzhen Xu, Lei Wang, et al. "PSRna: Prediction of small RNA secondary structures based on reverse complementary folding method." Journal of Bioinformatics and Computational Biology 14, no. 04 (2016): 1643001. http://dx.doi.org/10.1142/s0219720016430010.

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Prediction of RNA secondary structures is an important problem in computational biology and bioinformatics, since RNA secondary structures are fundamental for functional analysis of RNA molecules. However, small RNA secondary structures are scarce and few algorithms have been specifically designed for predicting the secondary structures of small RNAs. Here we propose an algorithm named “PSRna” for predicting small-RNA secondary structures using reverse complementary folding and characteristic hairpin loops of small RNAs. Unlike traditional algorithms that usually generate multi-branch loops an

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16

Guney, Kerim, and Ali Durmus. "Elliptical Antenna Array Synthesis Using Backtracking Search Optimisation Algorithm." Defence Science Journal 66, no. 3 (2016): 272. http://dx.doi.org/10.14429/dsj.66.9583.

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The design of the elliptical antenna arrays is relatively new research area in the antenna array community. Backtracking search optimisation algorithm (BSA) is employed for the synthesis of elliptical antenna arrays having different number of array elements. For this aim, BSA is used to calculate the optimum angular position and amplitude values of the array elements. BSA is a population-based iterative evolutionary algorithm. The remarkable properties of BSA are that it has a good optimisation performance, simple implementation structure, and few control parameters. The results of BSA are com

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17

Zhang, Jiahui, Xiao Wang, Mingchi Ju, Tailin Han, and Yingzhi Wang. "An Improved Sparsity Adaptive Matching Pursuit Algorithm and Its Application in Shock Wave Testing." Mathematical Problems in Engineering 2021 (July 24, 2021): 1–10. http://dx.doi.org/10.1155/2021/6615584.

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In the compressed sensing (CS) reconstruction algorithms, the problems of overestimation and large redundancy of candidate atoms will affect the reconstruction accuracy and probability of the algorithm when using Sparsity Adaptive Matching Pursuit (SAMP) algorithm. In this paper, we propose an improved SAMP algorithm based on a double threshold, candidate set reduction, and adaptive backtracking methods. The algorithm uses the double threshold variable step-size method to improve the accuracy of sparsity judgment and reduces the undetermined atomic candidate set in the small step stage to enha

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18

BRUYNOOGHE, MAURICE. "PROGRAMMING PEARL: Enhancing a search algorithm to perform intelligent backtracking." Theory and Practice of Logic Programming 4, no. 3 (2004): 371–80. http://dx.doi.org/10.1017/s1471068403001893.

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This paper illustrates how a Prolog program, using chronological backtracking to find a solution in some search space, can be enhanced to perform intelligent backtracking. The enhancement crucially relies on the impurity of Prolog that allows a program to store information when a dead end is reached. To illustrate the technique, a simple search program is enhanced.

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19

Hou, Ai Min, Chuan Fu Hu, and Zhi Feng Hao. "A General Vertex Partition Refinement Algorithm for Graph Isomorphism." Advanced Materials Research 760-762 (September 2013): 1919–24. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1919.

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A general depth-first backtracking algorithm for graph isomorphism with the vertex partition and refinement technique is presented in this paper. The time complexity of this nondeterministic polynomial algorithm is O(nα+3) where nα is the number of backtracking points and (h-1)/2α (h+1)/2 for h=logn in the worst cases. The tests on many types of graphs validated the efficiency of this algorithm for graph isomorphism.

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20

Torres-Jimenez, Jose, and Idelfonso Izquierdo-Marquez. "A Simulated Annealing Algorithm to Construct Covering Perfect Hash Families." Mathematical Problems in Engineering 2018 (July 26, 2018): 1–14. http://dx.doi.org/10.1155/2018/1860673.

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Covering perfect hash families (CPHFs) are combinatorial designs that represent certain covering arrays in a compact way. In previous works, CPHFs have been constructed using backtracking, tabu search, and greedy algorithms. Backtracking is convenient for small CPHFs, greedy algorithms are appropriate for large CPHFs, and metaheuristic algorithms provide a balance between execution time and quality of solution for small and medium-size CPHFs. This work explores the construction of CPHFs by means of a simulated annealing algorithm. The neighborhood function of this algorithm is composed of thre

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21

Crispin, Alan J., and Kai Cheng. "Backtracking Greedy Algorithm for Cutting Stock Problems." Applied Mechanics and Materials 10-12 (December 2007): 203–7. http://dx.doi.org/10.4028/www.scientific.net/amm.10-12.203.

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This paper presents a greedy search placement algorithm which incorporates backtracking for the leather stock cutting problem. In the leather manufacturing industry the efficient cutting of component parts (stencils) form a hide is of prime importance to maintain profitability. Consequently, the development of new approaches for generating cut-plans that minimise material waste and which can handle problem constraints have practical value. The unique feature of the greedy placement algorithm method presented in this paper is that it incorporates backtracking which allows previous placement ste

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22

YANG, Hai-Rong, Hong FANG, Cheng ZHANG, and Sui WEI. "Iterative Hard Thresholding Algorithm Based on Backtracking." Acta Automatica Sinica 37, no. 3 (2011): 276–82. http://dx.doi.org/10.3724/sp.j.1004.2011.00276.

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23

Chiou, Ji-Pyng, Chung-Fu Chang, and Chin-Ju Li. "A backtracking evolutionary algorithm for power systems." MATEC Web of Conferences 119 (2017): 01046. http://dx.doi.org/10.1051/matecconf/201711901046.

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24

Sellheim, P. "Backtracking algorithm for lepton reconstruction with HADES." Journal of Physics: Conference Series 599 (April 23, 2015): 012027. http://dx.doi.org/10.1088/1742-6596/599/1/012027.

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25

Zhao, Wenting, Lijin Wang, Yilong Yin, Bingqing Wang, and Yuchun Tang. "Sequential quadratic programming enhanced backtracking search algorithm." Frontiers of Computer Science 12, no. 2 (2018): 316–30. http://dx.doi.org/10.1007/s11704-016-5556-9.

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26

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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27

Dai, Qun, and Zhuan Liu. "ModEnPBT: A Modified Backtracking Ensemble Pruning algorithm." Applied Soft Computing 13, no. 11 (2013): 4292–302. http://dx.doi.org/10.1016/j.asoc.2013.06.023.

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28

Utama, Febri, Awang Harsa Kridalaksana, and Indah Fitri Astuti. "Implementasi Backtracking Algorithm Untuk Penyelesaian Permainan Su Doku Pola 9x9." Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer 11, no. 1 (2016): 29. http://dx.doi.org/10.30872/jim.v11i1.200.

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Permainan Su Dokupertama kali muncul pada tahun 1979 di majalah Dell Magazines dengan nama “Number Places”, kemudian menjadi populer dengan nama “Su Doku” di Jepang pada tahun 1984. Pada umumnya permainan ini terdiri dari 81 kotak kecil (sel), yang disebut juga dengan Su Dokupola 9x9. Su Doku ini dibagi menjadi 9 grid dengan pola 3x3. Diantarasel-sel tersebut terdapat angka 1 sampai dengan 9 sebagai angka awal. Angka-angka awal ini digunakan sebagai pembatas, sehingga kita hanya melanjutkan dengan mengisi angka hingga seluruh sel-sel terisi penuh masing-masing dengan angka 1 sampai dengan 9. M

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29

Korostil, Alexander V., and Andrei V. Nikolaev. "Backtracking Algorithms for Constructing the Hamiltonian Decomposition of a 4-regular Multigraph." Modeling and Analysis of Information Systems 28, no. 1 (2021): 6–21. http://dx.doi.org/10.18255/1818-1015-2021-1-6-21.

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We consider a Hamiltonian decomposition problem of partitioning a regular graph into edge-disjoint Hamiltonian cycles. It is known that verifying vertex non-adjacency in the 1-skeleton of the symmetric and asymmetric traveling salesperson polytopes is an NP-complete problem. On the other hand, a suffcient condition for two vertices to be non-adjacent can be formulated as a combinatorial problem of finding a Hamiltonian decomposition of a 4-regular multigraph. We present two backtracking algorithms for verifying vertex non-adjacency in the 1-skeleton of the traveling salesperson polytope and co

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30

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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31

Pal, Kamalendu. "RFID Tag Collision Problem in Supply Chain Management." International Journal of Advanced Pervasive and Ubiquitous Computing 11, no. 3 (2019): 1–12. http://dx.doi.org/10.4018/ijapuc.2019070101.

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The use of Radio Frequency Identification (RFID) technology has attracted a huge attention from the supply chain business community. This is due to the use of RFID technology wide range of applications in the fields of logistics and supply chain management. This paper presents a brief overview of a simple industrial RFID system and then describe the basic concept of tag collision problem. Despite many useful applications, the RFID tag collision creates a major problem for fast tag identification process. There are different algorithmic solutions available to overcome tag collision problem in i

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32

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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33

Zhou, Chong, Shengjie Li, Yuhe Zhang, Zhikun Chen, and Cuijun Zhang. "Enhancing Backtracking Search Algorithm using Reflection Mutation Strategy Based on Sine Cosine." Algorithms 12, no. 11 (2019): 225. http://dx.doi.org/10.3390/a12110225.

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Backtracking Search Algorithm (BSA) is a younger population-based evolutionary algorithm and widely researched. Due to the introduction of historical population and no guidance toward to the best individual, BSA does not adequately use the information in the current population, which leads to a slow convergence speed and poor exploitation ability of BSA. To address these drawbacks, a novel backtracking search algorithm with reflection mutation based on sine cosine is proposed, named RSCBSA. The best individual found so far is employed to improve convergence speed, while sine and cosine math mo

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34

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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35

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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36

Shaheen, Abdullah M., Ragab A. El-Sehiemy, and Sobhy M. Farrag. "Optimal reactive power dispatch using backtracking search algorithm." Australian Journal of Electrical and Electronics Engineering 13, no. 3 (2016): 200–210. http://dx.doi.org/10.1080/1448837x.2017.1325134.

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37

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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38

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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39

Zeine, Ahmed Tchvagha, Emmanuel Pagnacco, and Rachid Ellaia. "Backtracking immune algorithm for continuous multi-objective optimisation." International Journal of Mathematical Modelling and Numerical Optimisation 10, no. 3 (2020): 287. http://dx.doi.org/10.1504/ijmmno.2020.10030437.

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Zeine, Ahmed Tchvagha, Emmanuel Pagnacco, and Rachid Ellaia. "Backtracking immune algorithm for continuous multi-objective optimisation." International Journal of Mathematical Modelling and Numerical Optimisation 10, no. 3 (2020): 287. http://dx.doi.org/10.1504/ijmmno.2020.108619.

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41

Shang, Feng, James G. Uber, and Marios M. Polycarpou. "Particle Backtracking Algorithm for Water Distribution System Analysis." Journal of Environmental Engineering 128, no. 5 (2002): 441–50. http://dx.doi.org/10.1061/(asce)0733-9372(2002)128:5(441).

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42

Zeine, Ahmed Tchvagha, Abdelkhalak El Hami, Rachid Ellaia, and Emmanuel Pagnacco. "Backtracking search algorithm for multi-objective design optimisation." International Journal of Mathematical Modelling and Numerical Optimisation 8, no. 2 (2017): 93. http://dx.doi.org/10.1504/ijmmno.2017.086793.

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Pagnacco, Emmanuel, Rachid Ellaia, Abdelkhalak El Hami, and Ahmed Tchvagha Zeine. "Backtracking search algorithm for multi-objective design optimisation." International Journal of Mathematical Modelling and Numerical Optimisation 8, no. 2 (2017): 93. http://dx.doi.org/10.1504/ijmmno.2017.10007727.

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Pare, S., A. K. Bhandari, A. Kumar, and V. Bajaj. "Backtracking search algorithm for color image multilevel thresholding." Signal, Image and Video Processing 12, no. 2 (2017): 385–92. http://dx.doi.org/10.1007/s11760-017-1170-z.

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Chen, Debao, Feng Zou, Renquan Lu, and Peng Wang. "Learning backtracking search optimisation algorithm and its application." Information Sciences 376 (January 2017): 71–94. http://dx.doi.org/10.1016/j.ins.2016.10.002.

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46

Kartite, Jihane, and Mohamed Cherkaoui. "Improved backtracking search algorithm for renewable energy system." Energy Procedia 141 (December 2017): 126–30. http://dx.doi.org/10.1016/j.egypro.2017.11.024.

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47

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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48

Dardani, Ian, and Gerard F. Jones. "Algorithms for optimization of branching gravity-driven water networks." Drinking Water Engineering and Science 11, no. 1 (2018): 67–85. http://dx.doi.org/10.5194/dwes-11-67-2018.

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Abstract. The design of a water network involves the selection of pipe diameters that satisfy pressure and flow requirements while considering cost. A variety of design approaches can be used to optimize for hydraulic performance or reduce costs. To help designers select an appropriate approach in the context of gravity-driven water networks (GDWNs), this work assesses three cost-minimization algorithms on six moderate-scale GDWN test cases. Two algorithms, a backtracking algorithm and a genetic algorithm, use a set of discrete pipe diameters, while a new calculus-based algorithm produces a co

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49

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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50

Cen, Yigang, Fangfei Wang, Ruizhen Zhao, et al. "Tree-Based Backtracking Orthogonal Matching Pursuit for Sparse Signal Reconstruction." Journal of Applied Mathematics 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/864132.

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Compressed sensing (CS) is a theory which exploits the sparsity characteristic of the original signal in signal sampling and coding. By solving an optimization problem, the original sparse signal can be reconstructed accurately. In this paper, a new Tree-based Backtracking Orthogonal Matching Pursuit (TBOMP) algorithm is presented with the idea of the tree model in wavelet domain. The algorithm can convert the wavelet tree structure to the corresponding relations of candidate atoms without any prior information of signal sparsity. Thus, the atom selection process will be more structural and th

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