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Algorithms, Volume 8, Issue 1 (March 2015) , Pages 1-81

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Open AccessArticle
The Parameterized Complexity of the Rainbow Subgraph Problem
Algorithms 2015, 8(1), 60-81; https://doi.org/10.3390/a8010060 - 27 Feb 2015
Cited by 2 | Viewed by 2467
Abstract
The NP-hard RAINBOW SUBGRAPH problem, motivated from bioinformatics, is to find in an edge-colored graph a subgraph that contains each edge color exactly once and has at most \(k\) vertices. We examine the parameterized complexity of RAINBOW SUBGRAPH for paths, trees, and general [...] Read more.
The NP-hard RAINBOW SUBGRAPH problem, motivated from bioinformatics, is to find in an edge-colored graph a subgraph that contains each edge color exactly once and has at most \(k\) vertices. We examine the parameterized complexity of RAINBOW SUBGRAPH for paths, trees, and general graphs. We show that RAINBOW SUBGRAPH is W[1]-hard with respect to the parameter \(k\) and also with respect to the dual parameter \(\ell:=n-k\) where \(n\) is the number of vertices. Hence, we examine parameter combinations and show, for example, a polynomial-size problem kernel for the combined parameter \(\ell\) and ``maximum number of colors incident with any vertex''. Additionally, we show APX-hardness even if the input graph is a properly edge-colored path in which every color occurs at most twice. Full article
Open AccessArticle
Recognition of Unipolar and Generalised Split Graphs
Algorithms 2015, 8(1), 46-59; https://doi.org/10.3390/a8010046 - 13 Feb 2015
Cited by 6 | Viewed by 2418
Abstract
A graph is unipolar if it can be partitioned into a clique and a disjoint union of cliques, and a graph is a generalised split graph if it or its complement is unipolar. A unipolar partition of a graph can be used to [...] Read more.
A graph is unipolar if it can be partitioned into a clique and a disjoint union of cliques, and a graph is a generalised split graph if it or its complement is unipolar. A unipolar partition of a graph can be used to find efficiently the clique number, the stability number, the chromatic number, and to solve other problems that are hard for general graphs. We present an O(n2)-time algorithm for recognition of n-vertex generalised split graphs, improving on previous O(n3)-time algorithms. Full article
Open AccessArticle
An Efficient SAR Image Segmentation Framework Using Transformed Nonlocal Mean and Multi-Objective Clustering in Kernel Space
Algorithms 2015, 8(1), 32-45; https://doi.org/10.3390/a8010032 - 09 Feb 2015
Cited by 1 | Viewed by 2803
Abstract
Synthetic aperture radar (SAR) image segmentation usually involves two crucial issues: suitable speckle noise removing technique and effective image segmentation methodology. Here, an efficient SAR image segmentation method considering both of the two aspects is presented. As for the first issue, the famous [...] Read more.
Synthetic aperture radar (SAR) image segmentation usually involves two crucial issues: suitable speckle noise removing technique and effective image segmentation methodology. Here, an efficient SAR image segmentation method considering both of the two aspects is presented. As for the first issue, the famous nonlocal mean (NLM) filter is introduced in this study to suppress the multiplicative speckle noise in SAR image. Furthermore, to achieve a higher denoising accuracy, the local neighboring pixels in the searching window are projected into a lower dimensional subspace by principal component analysis (PCA). Thus, the nonlocal mean filter is implemented in the subspace. Afterwards, a multi-objective clustering algorithm is proposed using the principals of artificial immune system (AIS) and kernel-induced distance measures. The multi-objective clustering has been shown to discover the data distribution with different characteristics and the kernel methods can improve its robustness to noise and outliers. Experiments demonstrate that the proposed method is able to partition the SAR image robustly and accurately than the conventional approaches. Full article
(This article belongs to the Special Issue Advanced Data Processing Algorithms in Engineering)
Open AccessArticle
An Improved Shuffled Frog-Leaping Algorithm for Flexible Job Shop Scheduling Problem
Algorithms 2015, 8(1), 19-31; https://doi.org/10.3390/a8010019 - 04 Feb 2015
Cited by 9 | Viewed by 3381
Abstract
The flexible job shop scheduling problem is a well-known combinatorial optimization problem. This paper proposes an improved shuffled frog-leaping algorithm to solve the flexible job shop scheduling problem. The algorithm possesses an adjustment sequence to design the strategy of local searching and an [...] Read more.
The flexible job shop scheduling problem is a well-known combinatorial optimization problem. This paper proposes an improved shuffled frog-leaping algorithm to solve the flexible job shop scheduling problem. The algorithm possesses an adjustment sequence to design the strategy of local searching and an extremal optimization in information exchange. The computational result shows that the proposed algorithm has a powerful search capability in solving the flexible job shop scheduling problem compared with other heuristic algorithms, such as the genetic algorithm, tabu search and ant colony optimization. Moreover, the results also show that the improved strategies could improve the performance of the algorithm effectively. Full article
(This article belongs to the Special Issue Advanced Data Processing Algorithms in Engineering)
Open AccessArticle
Networked Control System Time-Delay Compensation Based on Time-Delay Prediction and Improved Implicit GPC
Algorithms 2015, 8(1), 3-18; https://doi.org/10.3390/a8010003 - 19 Jan 2015
Cited by 13 | Viewed by 2783
Abstract
The random time delay in a networked control system can usually deteriorate the control performance and stability of the networked control system. In order to solve this problem, this paper puts forward a networked control system random time-delay compensation method based on time-delay [...] Read more.
The random time delay in a networked control system can usually deteriorate the control performance and stability of the networked control system. In order to solve this problem, this paper puts forward a networked control system random time-delay compensation method based on time-delay prediction and improved implicit generalized predictive control (GPC). The least squares support vector machine is used to predict the future time delay of network. The parameters of the least squares support vector machine time-delay prediction model are difficult to determine, and the genetic algorithm is used for least squares support vector machine optimal prediction parameter optimization. Then, an improved implicit generalized predictive control method is adopted to compensate for the time delay. The simulation results show that the method in this paper has high prediction accuracy and a good compensation effect for the random time delay of the networked control system, has a small amount of on-line calculation and that the output response and control stability of the system are improved. Full article
Open AccessEditorial
Acknowledgement to Reviewers of Algorithms in 2014
Algorithms 2015, 8(1), 1-2; https://doi.org/10.3390/a8010001 - 08 Jan 2015
Viewed by 2024
Abstract
The editors of Algorithms would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2014:[...] Full article
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