An Efficient Ant Algorithm for Swarm-Based Image Clustering

Ouadfel, Salima; Batouche, Mohamed

doi:10.3844/jcssp.2007.162.167

Cited by 23 publications

(17 citation statements)

References 5 publications

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“…Salima Ouadfel and Mohamed Batouche [67] have presented an artificial ant clustering algorithm (AntClust), a new ant-based algorithm for image segmentation that uses the self-organizing and autonomous brood sorting behavior observed in real ants.…”

Section: Ant-based Clustering In the Literaturementioning

confidence: 99%

“…Initially, the Deneubourg and LF algorithms have become well-known models that have been used in applications like data mining [60] [79] and graph-partitioning [47]. Several papers have reported successful applications of ant-based clustering algorithms such as Data retrieval and textual document clustering [63], Classification of stone images [64], Web usage mining [2] [31], Solvency prediction [87], Network traffic analysis [72], Intrusion detection [66], Long-term electrocardiogram processing [55], Textural defect detection [11], Knowledge discovery in DNA chip analysis data [54], Distributed databases [14], Biomedical data processing [53], Image segmentation [67] [81] [83], Human skin analysis and generating portal site [31], Image retrieval [30], Content-based image retrieval [73], Data clustering [75], Clustering and classification [26], Computer forensics [17] and Gene expression data analysis [85].…”

Section: Applicationsmentioning

confidence: 99%

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Ant-based Clustering Algorithms: A Brief Survey

Jafar¹,

Sivakumar²

2010

IJCTE

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Abstract-Ant-based clustering is a biologically inspired data clustering technique. Clustering task aims at the unsupervised classification of patterns in different groups. Clustering problem has been approached from different disciplines during last years. In recent years, many algorithms have been developed for solving numerical and combinatorial optimization problems. Most promising among them are swarm intelligence algorithms. Clustering with swarm-based algorithms is emerging as an alternative to more conventional clustering techniques. These algorithms have recently been shown to produce good results in a wide variety of real-world applications. During the last five years, research on and with the ant-based clustering algorithms has reached a very promising state. In this paper, a brief survey on ant-based clustering algorithms is described. We also present some applications of ant-based clustering algorithms.

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Section: Ant-based Clustering In the Literaturementioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Ant-based Clustering Algorithms: A Brief Survey

Jafar¹,

Sivakumar²

2010

IJCTE

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“…In this way, ants cluster pixels into distinctive independent groups. Quadfel and Batouche [1] demonstrated the ability of AntClust to extract the correct number of clusters and to give better clustering quality compared to those obtained from Kmeans algorithm. To take advantage of Kmeans and AntClust and avoid their drawbacks, this paper propose a new hybrid algorithm that executes the AntClust algorithm with a limited number of iterations, then speeding up convergence with the Kmeans algorithm and using hierarchical clustering on heaps of objects.…”

Section: Introductionmentioning

confidence: 99%

“…Its purpose is to subdivide an image into meaningful nonoverlapping regions [1]. It may consist of two processes: recognition and delineation.…”

Section: Introductionmentioning

confidence: 99%

AntHeaps: A New Hybrid Image Segmentation Algorithm using Ant Colonies

Reda¹,

Abdel-Galil²,

Ghalwash³

2013

IJCA

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During the last few years, the Segmentation problem has been tackled from different disciplines. Many algorithms have been developed to solve this problem. AntClust algorithm is an antbased algorithm that uses the self-organizing and autonomous brood sorting behavior observed in real ants for unsupervised partitioning. A population of artificial ants provides an image segmentation of the relevant classes without any previous knowledge about the number of classes needed. This paper proposes a hybrid solution based on AntClust algorithm and data mining (e.g., Kmeans). Experimental results demonstrate that the proposed solution is able to extract the correct number of clusters with better clustering quality and execution time compared to the results obtained from AntClust algorithm.

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“…The notion of stigmergy means the indirect communication of individuals through modifying their environment. Several algorithms which are based on ant colony problems were introduced in recent years to solve different problems, e.g., optimization problems, image segmentation (Ouadfel and Batouche, 2007;Ibrahim et al, 2005;Hashim and Abdl, 2010).…”

Section: Introductionmentioning

confidence: 99%

Enhanced Clustering Techniques for Hyper Network Planning using Minimum Spanning Trees and Ant-Colony Algorithm

Ibrahim¹

2011

Journal of Computer Science

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Problem statement:The process of network planning is divided into two sub steps. The first step is determining the location of the Multi Service Access Node (MSAN). The second step is the construction of subscriber network lines from MSAN to subscribers to satisfy optimization criteria and design constraints. Due to the complexity of this process artificial intelligence and clustering techniques have been successfully deployed to solve many problems. The problems of the locations of MSAN, the cabling layout and the computation of optimum cable network layouts have been addressed in this study. The proposed algorithm, Clustering density-Based Spatial of Applications with Noise original, minimal Spanning tree and modified Ant-Colony-Based algorithm (CBSCAN-SP-ANT), used two clustering algorithms which are density-based and agglomerative clustering algorithm using distances which are shortest paths distance and satisfying the network constraints. This algorithm used wire and wireless technology to serve the subscribers demand and place the switches in a real optimal place. Approach: The density-based Spatial Clustering of Applications with Noise original (DBSCAN) algorithm has been modified and a new algorithm (NetPlan algorithm) has been proposed by the author in a recent work to solve the first step in the problem of network planning. In the present study, the NetPlan algorithm is modified by introduce the modified Ant-Colony-Based algorithm to find the optimal path between any node and the corresponding MSAN node in the first step of network planning process to determine nodes belonging to each cluster. The second step, in the process of network planning, is also introduced in the present study. For each cluster, the optimal cabling layout from each MSAN to the subscriber premises is determining by introduce the Prime algorithm which construct minimal spanning tree. Results: Experimental results and analysis indicate that the CBSCAN-SP-ANT algorithm was effective, leads to minimum costs for network construction and make the best grade of service. Conclusion: Using mobile network to serve the area with low density is decreasing the cost of design the fixed wire network. Also, using the modified ANT algorithm and minimum spanning tree, are helping to construct the cable layout from each MSAN to subscribers when the network is complicated and the number of intersections and streets are large.

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An Efficient Ant Algorithm for Swarm-Based Image Clustering

Cited by 23 publications

References 5 publications

Ant-based Clustering Algorithms: A Brief Survey

Ant-based Clustering Algorithms: A Brief Survey

AntHeaps: A New Hybrid Image Segmentation Algorithm using Ant Colonies

Enhanced Clustering Techniques for Hyper Network Planning using Minimum Spanning Trees and Ant-Colony Algorithm

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