Level set evolution with sparsity constraint for object extraction

Liu, Guoqi; Zou, Jian

doi:10.1049/iet-ipr.2017.0939

Cited by 19 publications

(11 citation statements)

References 49 publications

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“…The optimal distribution centers and distribution addressing schemes are shown in Tables 17 and 18. According to Tables 17 and 18, the optimal distribution center points found by CS algorithm for 6 and 10 distribution centers are (3,11,22,1,15,20) and (6,8,18,11,21,28,16,1,20,15). The optimal distribution center points found by IGA algorithm for 6 and 10 distribution centers are (10,22,21,2,20,17) and (30,23,14,1,2,11,25,24,15,4).…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 98%

“…The optimal distribution center points found by IGA algorithm for 6 and 10 distribution centers are (10,22,21,2,20,17) and (30,23,14,1,2,11,25,24,15,4). The optimal distribution center points found by CCS algorithm for 6 and 10 distribution centers are (23,22,21,16,15,20) and (6,10,23,14,22,25,7,16,15,20).…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

“…Optimization problems have been one of the most important research topics in recent years. They exist in many domains, such as scheduling [1,2], image processing [3][4][5][6], feature selection [7][8][9] and detection [10], path planning [11,12], feature selection [13], cyber-physical social system [14,15], texture discrimination [16], saliency detection [17], classification [18,19], object extraction [20], shape design [21], big data and large-scale optimization [22,23], multi-objective optimization [24], knapsack problem [25][26][27], fault diagnosis [28][29][30], and test-sheet composition [31]. Metaheuristic algorithms [32], a theoretical tool, are based on nature-inspired ideas, which have been extensively used to solve highly non-linear complex multi-objective optimization problems [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Xiao

Lei

et al. 2020

Mathematics

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Cuckoo search (CS) algorithm is a novel swarm intelligence optimization algorithm, which is successfully applied to solve some optimization problems. However, it has some disadvantages, as it is easily trapped in local optimal solutions. Therefore, in this work, a new CS extension with Q-Learning step size and genetic operator, namely dynamic step size cuckoo search algorithm (DMQL-CS), is proposed. Step size control strategy is considered as action in DMQL-CS algorithm, which is used to examine the individual multi-step evolution effect and learn the individual optimal step size by calculating the Q function value. Furthermore, genetic operators are added to DMQL-CS algorithm. Crossover and mutation operations expand search area of the population and improve the diversity of the population. Comparing with various CS algorithms and variants of differential evolution (DE), the results demonstrate that the DMQL-CS algorithm is a competitive swarm algorithm. In addition, the DMQL-CS algorithm was applied to solve the problem of logistics distribution center location. The effectiveness of the proposed method was verified by comparing with cuckoo search (CS), improved cuckoo search algorithm (ICS), modified chaos-enhanced cuckoo search algorithm (CCS), and immune genetic algorithm (IGA) for both 6 and 10 distribution centers.

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Section: Analysis Of Experimental Resultsmentioning

confidence: 98%

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Xiao

Lei

et al. 2020

Mathematics

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show abstract

“…The traditional method is difficult to solve these problems, so the intelligent algorithms are required to solve these problems. Inspired by nature, these strong metaheuristic algorithms are applied to solve NP-hard problems such as scheduling [3][4][5][6][7], image [8][9][10], feature selection [11][12][13] and detection [14][15][16], path planning [17,18], cyber-physical social system [19,20], texture discrimination [21], factor evaluation [22], saliency detection [23], classification [24,25], object extraction [26], gesture segmentation [27], economic load dispatch [28,29], shape design [30], big data and large-scale optimization [31][32][33], signal processing [34], silencing efficacy prediction [35], multi-objective optimization [36,37], unit commitment [38,39], vehicle routing [40], knapsack problem [41][42][43] and fault diagnosis [44][45][46], and test-sheet composition…”

Section: Introductionmentioning

confidence: 99%

Enhancing Whale Optimization Algorithm with Chaotic Theory for Permutation Flow Shop Scheduling Problem

Jiāng¹,

Li-hong²,

Li³

et al. 2021

IJCIS

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The permutation flow shop scheduling problem (PFSSP) is a typical production scheduling problem and it has been proved to be a nondeterministic polynomial (NP-hard) problem when its scale is larger than 3. The whale optimization algorithm (WOA) is a new swarm intelligence algorithm which performs well for PFSSP. But the stability is still low, and the optimization results are not too good. On this basis, we optimize the parameters of WOA through chaos theory, and put forward a chaotic whale algorithm (CWA). Firstly, in this paper, the proposed CWA is combined with Nawaz-Ensco-Ham (NEH) and largest-rank-value (LRV) rule to initialize the population. Next, chaos theory is applied to WOA algorithm to improve its convergence speed and stability. On this basis, we also use cross operator and reversal-insertion operator to enhance the search ability of the algorithm. Finally, the improved local search algorithm is used to optimize the job sequence to find the minimum makespan. In several experiments, different benchmarks are used to investigate the performance of CWA. The experimental results show that CWA has better performance than other scheduling algorithms.

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“…As we know, sparse reconstruction model can reconstruct images from highly undersampled observations accurately and solve the problems of limited acquisition data and slow acquisition time in MRI 2‐6 . The target of sparse reconstruction is mainly to find the sparse solution x of the underdetermined linear equations Ax = y , aiming at reconstructing the original images with highly undersampled observations 7‐12 . Sparse reconstruction model can be expressed as

\min_{x} \frac{1}{2} {‖y - Ax‖}_{2}^{2} + italicλφ ((), x),

where x ∈ ℝ N denotes the magnetic resonance image to be reconstructed, y ∈ ℝ M denotes the k‐space signal acquired by the magnetic resonance coil, A ∈ ℝ M × N denotes the undersampling Fourier transform and M < N , usually we have A = R × F , R ∈ ℝ M × N is an undersampling template, 13‐15 such as radial sampling template, variable density random sampling template, and Cartesian sampling template, etc., F ∈ ℝ N × N is a sparse Fourier operator, such as Fourier transform and wavelet transform, etc., φ ( x ) is used as regularizer term.…”

Section: Introductionmentioning

confidence: 99%

Magnetic resonance imaging reconstruction via non‐convex total variation regularization

Shen

Zhang

et al. 2020

Int J Imaging Syst Tech

Self Cite

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Magnetic resonance imaging (MRI) reconstruction model based on total variation (TV) regularization can deal with problems such as incomplete reconstruction, blurred boundary, and residual noise. In this article, a non‐convex isotropic TV regularization reconstruction model is proposed to overcome the drawback. Moreau envelope and minmax‐concave penalty are firstly used to construct the non‐convex regularization of L2 norm, then it is applied into the TV regularization to construct the sparse reconstruction model. The proposed model can extract the edge contour of the target effectively since it can avoid the underestimation of larger nonzero elements in convex regularization. In addition, the global convexity of the cost function can be guaranteed under certain conditions. Then, an efficient algorithm such as alternating direction method of multipliers is proposed to solve the new cost function. Experimental results show that, compared with several typical image reconstruction methods, the proposed model performs better. Both the relative error and the peak signal‐to‐noise ratio are significantly improved, and the reconstructed images also show better visual effects. The competitive experimental results indicate that the proposed approach is not limited to MRI reconstruction, but it is general enough to be used in other fields with natural images.

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Level set evolution with sparsity constraint for object extraction

Cited by 19 publications

References 49 publications

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Enhancing Whale Optimization Algorithm with Chaotic Theory for Permutation Flow Shop Scheduling Problem

Magnetic resonance imaging reconstruction via non‐convex total variation regularization

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