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	1. A Elimination Mechanism of Glue Variables for Solving SAT Problems in Linguistics
	ZHANG Ziwei,ZHANG Yang
	Computer Science and Technology 04 February 2021
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	Abstract:We propose GVE(Glue Variables Elimination), an algorithm that organically combines neural networks with a deterministic solver to solve SAT(Boolean satisfiability problem) in the filed of linguistics. It gives full play to their respective advantages by following steps: (a) finding the glue variables of the problem; (b) determining their values; (c) simplifying the original formula; (d) using a deterministic solver to solve the simplified problem. We use SATCOMP 2003-2019 benchmarks as the test data sets, and compare our model with the SAT solver CADICAL that has performed well in SATCOMP 2019 as well as the neural network models proposed in recent years. GVE model shows good performance. As the complexity of the problem increases, the solution time is much better than the deterministic solver, while at the same time more accurate than other neural network models.
	TO cite this article:ZHANG Ziwei,ZHANG Yang. A Elimination Mechanism of Glue Variables for Solving SAT Problems in Linguistics[OL].[ 4 February 2021] http://en.paper.edu.cn/en_releasepaper/content/4753646


	2. Large-scale global optimization using cooperative coevolution with self-adaptive differential grouping
	FANG Wei,FANG Wei,MIN Ruigao,ZHOU Jianhong,ZHOU Jianhong,ZHOU Jianhong
	Computer Science and Technology 05 December 2018
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	Abstract:Cooperative co-evolution (CC) is a popular evolutionary computation approach that can divide a large problem into a set of smaller sub-problems and solve them independently. CC has been an important divide-and-conquer algorithm for large-scale global optimization (LSGO) problems. Identification of variable interactions is the main challenge in CC to decompose the LSGO problems. Differential Grouping (DG) is a competitive variable grouping algorithm that can address the non-separable components of a continuous problem. As an improved version of DG, Global Differential Grouping (GDG) addresses the drawbacks of DG which are variables interactions missing during grouping and grouping performance sensitive to the threshold. In this paper, a Self-adaptive Differential Grouping (SDG) based on GDG is proposed in order to further improve the grouping accuracy on the CEC'2010 LSGO benchmark suite. The threshold for grouping in SDG can adjust adaptively along with the magnitude of different functions and is determined by only two points which is a randomly sampled point and its corresponding opposite point in the decision space. A self-adaptive pyramid allocation (SPA) strategy that can allocate different computational resource to subcomponents is also studied in this paper. The proposed algorithm, where SDG and SPA working with the optimizer $SaNSDE$ (CCSPA-SDG), is used to optimize the CEC'2010 LSGO benchmark suite. Experimental results show that SDG achieved ideal decomposition of the variables for all the CEC'2010 LSGO benchmark functions. The optimization performance of CCSPA-SDG also outperforms the state-of-the-art results.
	TO cite this article:FANG Wei,FANG Wei,MIN Ruigao, et al. Large-scale global optimization using cooperative coevolution with self-adaptive differential grouping[OL].[ 5 December 2018] http://en.paper.edu.cn/en_releasepaper/content/4746608

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	3. A Technology Based on Melody Key Points in Query by Humming
	LV Anchao,LIU Gang
	Computer Science and Technology 26 December 2017
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	Abstract:As one of the important modules in query by humming system, humming feature extraction aims to extract the stable melody features. There are three typical personalization factors lead to instability of humming features, which are the base pitch shifting, the dynamic range changing and the humming rate changing. According to the characteristic of inconsistent humming rate, this paper mines the key point information that is stable to the change of melody rhythm. The melody is segmented and index is created first according to the key points, and the features in the segment are extracted. Then, a feature extraction method based on key point segmentation is proposed. Besides, using the theory of music theory and statistics, this paper takes the non-uniform segmentation statistic features and rhythm statistic features as the final humming features. Finally, numerical results are carried out to evaluate the validity and rapidity of the proposed schemes.
	TO cite this article:LV Anchao,LIU Gang. A Technology Based on Melody Key Points in Query by Humming[OL].[26 December 2017] http://en.paper.edu.cn/en_releasepaper/content/4742916


	4. An Ant Colony System Based Virtual Network Embedding Algorithm
	Jia-Bin Wang,Wei-Neng Chen
	Computer Science and Technology 02 May 2017
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	Abstract:The virtual networking embedding (VNE) problem is a core issue in network virtualization. This is also a challenging problem as it contains different kinds of constraints, and its complexity becomes even higher in an online VNE problem with thousands of virtual network (VN) requests. In this paper, we proposed an ant colony system based VNE algorithm, called ACS-VNE, for the online VNE problem. The benefits of ACS-VNE are threefold. First, it is an ACS based algorithm so it can take full advantage of the dynamically changing heuristic information and pheromone to improve the quality of a solution. Second, different from previous work that only considers the resource of nodes in node mapping phase, we take the distance message related to links into consideration so that we can reduce the cost of VN requests. The last but not least, the algorithm tries to reduce the cost for every single VN and it helps to increase the possibility of accepting more future VN requests. The proposed method is tested on both the single VN request VNE problem and the online VNE problem. Experimental results show that the proposed algorithm outperforms some previous approaches in terms of average revenue and acceptance ratio, and the results also have a relatively low cost.
	TO cite this article:Jia-Bin Wang,Wei-Neng Chen. An Ant Colony System Based Virtual Network Embedding Algorithm[OL].[ 2 May 2017] http://en.paper.edu.cn/en_releasepaper/content/4729688


	5. Parameterized Approximation Algorithms for the Shallow-Light Steiner Tree Problem
	GUO Longkun, LIAO Kewen, SHEN Hong
	Computer Science and Technology 02 June 2015
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	Abstract:Let $G=(V,, E)$ be a given graph with non-negative integral edgecost and delay, $Ssubseteq V$ be a terminal set and $rin S$ bethe selected root. The shallow-light Steiner tree (emph{SLST}) problemis to compute a minimum cost tree spanning the terminals of $S$,such that the delay between $r$ and every other terminal is boundedby a given delay constraint $Dinmathbb{Z}_{0}^{+}$. It is knownthat the emph{SLST} problem is NP-hard and unless $NPsubseteq DTIME(n^{loglog n})$there exists no approximation algorithm with ratio $(1,,gammalog^{2}n)$for some fixed $gamma>0$. Nevertheless,under the same assumption it admits no approximation ratio betterthan $(1,,gammalog\|V\|)$ for some fixed $gamma>0$ even when $D=2$.This paper first gives an exact algorithm with time complexity$O(3^{t}nD+2^{t}n^{2}D^{2}+n^{3}D^{3})$,where $n$ and $t$ are the numbers of vertices and terminals of thegiven graph respectively. This is a pseudo polynomial time parameterizedalgorithm with respect to the parameterization ``number of terminals''.Later, this algorithm is improved to a parameterized approximationalgorithm with a time complexity $O(3^{t}rac{n^{2}}{epsilon}+2^{t}rac{n^{4}}{epsilon^{2}}+rac{n^{6}}{epsilon^{3}})$and a bifactor approximation ratio $(1+epsilon,,1)$. That is, forany small real number $epsilon>0$, the algorithm computes a Steinertree with delay and cost bounded by $(1+epsilon)D$ and the optimumcost respectively.
	TO cite this article:GUO Longkun, LIAO Kewen, SHEN Hong . Parameterized Approximation Algorithms for the Shallow-Light Steiner Tree Problem[OL].[ 2 June 2015] http://en.paper.edu.cn/en_releasepaper/content/4640692


	6. Approximating Algorithms for Computing Energy Constrained MinimumCost Steiner Trees
	ZOU Nianchen, GUO Longkun, HUANG Peihuang
	Computer Science and Technology 30 May 2015
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	Abstract:Green data transmission is important for wireless networks, such assensor networks, mobile networks, etc. This paper gives approximationalgorithms for constructing energy constrained minimum cost Steinertrees (ECMST), a topology for data multicast considering both savingenergy and minimizing the occupied resource. For a given undirectedgraph, in which each edge is with nonnegative integral cost and energyconsumption, the ECMST problem is to compute a minimum cost tree spanningall specified terminals and satisfying a given energy consumptionconstraint. Apparently ECMST is NP-hard, since it includes the minimumSteiner tree problem which is known NP-hard.The paper first presents an approximation algorithm by extendingByrka et al.'s method via Lagrangian relaxation. Then, the paper improves the ratio of the approximation algorithm to $(2,,3)$by replacing components of the computed Steiner tree. The improvementis mainly based on three ingredients: a generalization of the $k$-Steinerratio against ECMST, the observation that ECMST is pseudo-polynomialsolvable when the number of the terminals are fixed, and the extensionof Byrka and et al.'s approximation algorithm. To the best of ourknowledge, our algorithm is with the best ratio in the current stateof the art. Although Ravi and Goemanshave proposed a $(1+epsilon,,1)$-approximation algorithm for thespecial case when all vertices are terminals, their method can notbe applied to ECMST, since their method is based on a matroid propertywhich doesn't hold for ECMST.
	TO cite this article:ZOU Nianchen, GUO Longkun, HUANG Peihuang. Approximating Algorithms for Computing Energy Constrained MinimumCost Steiner Trees[OL].[30 May 2015] http://en.paper.edu.cn/en_releasepaper/content/4640705


	7. Solving the Fixed Spectrum Frequency Assignment Problem via Iterated Tabu Search
	Zhipeng L"u, Zhaojing Luo, Tao Ye
	Computer Science and Technology 03 December 2014
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	Abstract:This paper presents an Iterated Tabu Search (ITS) algorithm for solving the fixed spectrum frequency assignment problem (FS-FAP), which integrates several distinguishing features, such as a novel dynamic tabu tenure mechanism and three kinds of perturbation operators. Computational results assessed on two sets of 47 public benchmark instances demonstrate the efficacy of the proposed ITS algorithm in terms of both solution quality and efficiency. Particularly, for the first set, ITS is able to find new best objective values for 16 instances while matching the previous best objective values for 21 ones, and for the COST 259 benchmark set, compared with reference algorithm, ITS obtains 2 better results in a reasonable time. Furthermore, several important ingredients of the ITS algorithm are also analyzed.
	TO cite this article:Zhipeng L"u, Zhaojing Luo, Tao Ye. Solving the Fixed Spectrum Frequency Assignment Problem via Iterated Tabu Search[OL].[ 3 December 2014] http://en.paper.edu.cn/en_releasepaper/content/4619809


	8. Memetic Search for the Bandwidth Coloring Problem
	LU Zhipeng
	Computer Science and Technology 18 November 2014
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	Abstract:This paper presents a memetic algorithm for solving the Bandwidth Coloring Problem (BCP) and the Bandwidth MultiColoring Problem (BMCP), which incorporates a tabu search algorithm into the framework of an evolutionary algorithm. The proposed algorithm is assessed on two sets of totally 66 benchmark instances commonly used in the literature. Computational results demonstrate that the memetic algorithm is highly competitive in terms of both solution quality and efficiency compared to some highly efficient algorithms in the literature. The investigations of some key elements of the proposed method show the importance of the tabu search algorithm and the perturbation strength for the performance of the algorithm.?????
	TO cite this article:LU Zhipeng. Memetic Search for the Bandwidth Coloring Problem[OL].[18 November 2014] http://en.paper.edu.cn/en_releasepaper/content/4616450


	9. Weighted Extension of OMP for Discriminative Dictionary Learning
	Chen Naheng
	Computer Science and Technology 10 July 2014
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	Abstract:Classification based on sparse representation has received a great deal of attention in recent years. In this paper, a weighted extension of OMP for classification is presented. It can be used in dictionary learning algorithms when dealing with classification issue. The proposed method adds a weighted parameter to the OMP algorithm; therefore discriminative power is enhanced and leads a faster convergence. This method is evaluated in the face recognition on Extended YaleB database and AR database. The result shows that the method achieves comparable recognition accuracy to the state-of-the-art result and runs 18 times faster in Extended YaleB and 30 times faster in AR.
	TO cite this article:Chen Naheng. Weighted Extension of OMP for Discriminative Dictionary Learning[OL].[10 July 2014] http://en.paper.edu.cn/en_releasepaper/content/4601431


	10. Task scheduling algorithm based on greedy strategy in cloud computing
	ZHOU Zhou,HU Zhigang
	Computer Science and Technology 18 March 2014
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	Abstract:In view of Min-Min algorithm prefers scheduling small tasks and Max-Min algorithm prefers scheduling big tasks led to problem of load imbalance in cloud computing, a new algorithm named Min-Max is proposed. Min-Max makes good use of time for greedy strategy, small tasks and big tasks are put together for scheduling in order to solve the problem of load imbalance. Experimental results show that the Min-Max improves the utilization rate of entire system and saves 9% overall execution time compared with Min-Min. As compared with Max-Min, Min-Max improves the utilization rate of entire system, the total completion time and average response time are saved 7% and 9% respectively.
	TO cite this article:ZHOU Zhou,HU Zhigang. Task scheduling algorithm based on greedy strategy in cloud computing[OL].[18 March 2014] http://en.paper.edu.cn/en_releasepaper/content/4590564

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