Ant Colony Clustering Research Articles

A Co-operative Game Theoretic Approach to Improve the Intrusion Detection System in a Network using Ant Colony Clustering

a network foolproof is a very important task that every Intrusion Detection System should provide to the network. Areas of deployment of an IDS is also an important task that helps in efficient functioning of the system. Deploying the IDS in all the systems is a very inefficient strategy that reduces the performance of the entire network, while deploying the IDS in inappropriate or inefficient nodes leads to the system becoming vulnerable to attacks. The current proposal deals with improving the effectiveness of an Intrusion Detection System by selecting the appropriate candidates in the network. The candidacy selection is performed by initially clustering the nodes using Ant Colony Clustering algorithm and using Game Theoretical approaches for selection of heads and monitoring the environment for changes.

The segmentation of wear particles in ferrograph images based on an improved ant colony algorithm

Ferrography is a notably useful means to determine the wear condition of machines. Before attempting to extract the feature parameters of wear particles for identification and analysis, it is necessary to separate wear particles in ferrograph images. Hence, wear particle segmentation is a critical first step for intelligent ferrography based on computer image analysis. This paper presents a new method for the segmentation of wear particles by combining watershed and an improved ant colony clustering algorithm. The experimental results have demonstrated the possibility of achieving accurate segmentation of wear particles, including large abnormal wear particles and deposited chains.

The Research of Key Factors Influencing Ecological Civilization Consciousness of College Students Based on Ant Colony Clustering Algorithm

Ecological civilization construction is related to not only peoples well-being, but also the long term plan of a countrys development and revitalization. This research applied ant colony clustering algorithm to study the feasibility and key factors of ecological civilization promotion in the college students It is the first time that quantitative method was introduced into the human consciousness research. The results show that college students have known the situation of resources and environmental problems well, which is a advantage for the promotion of ecological civilization concept. However, the role that colleges play in ecological civilization promotion has not performed fully. And above two items are the key factors to promote ecological civilization promotion.

Efficient and Robust Cluster Identification for Ultra-wideband Propagations Inspired byBiological Ant Colony Clustering

Cluster identification of ultra-wideband (UWB) propagations is of great significance to the parameter extraction and measurement analysis of channel modeling. In this paper, we address this challenging problem within a promising biological processing framework. Both the two large-scale characteristics of each multipath component, i.e., the decaying amplitude and the time of arrivals, are organically combined and fully explored in the suggested cluster identification algorithm. Each resolvable trajectory component is first projected onto a 2-D amplitude–time plane and further modeled as a virtual ant-agent, which can move around in this 2-D workspace with a preference to the high local-environment similarity. By establishing a subtle population similarity and specifying an efficient position adaptation strategy, cluster identifications can be realized by the biological ant colony clustering procedure. Owing to the population-based intelligence and the involved positive-feedback collaboration during the agents evolution, the suggested algorithm can efficiently identify the involved multiple clusters in a completely automatic manner. Experiments on UWB channels validate the proposed method. The practical parameter configuration is analyzed, and a group of numerical performance metrics is derived. As demonstrated by numerical investigations, multiple clusters involved in UWB channel impulse responses can be accurately extracted.

Core Business Selection Based on Ant Colony Clustering Algorithm

Core business is the most important business to the enterprise in diversified business. In this paper, we first introduce the definition and characteristics of the core business and then descript the ant colony clustering algorithm. In order to test the effectiveness of the proposed method, Tianjin Port Logistics Development Co., Ltd. is selected as the research object. Based on the current situation of the development of the company, the core business of the company can be acquired by ant colony clustering algorithm. Thus, the results indicate that the proposed method is an effective way to determine the core business for company.

The Improved Ant Colony Clustering Algorithm for Large-scale WSN in the ITS

In order to enhance the node’s energy efficiency of large-scale and multi-sink wireless sensor network in the intelligent transportation system, to prolong the network life cycle, we proposed a kind of improved colony algorithm for wireless sensor clustering and routing based on the analysis of the existing classical algorithm. This algorithm used threshold to elect cluster’s head and made the network clustering quickly in fascicles stage, its routing method is multiple hops mode based on the pheromone in the stage of routing. The simulation results show that after using this algorithm, the network energy consumption is lower, life cycle is longer, it is suitable for the application in the large-scale network environment.

Ant Colony Clustering Algorithm based on Information Entropy

Ant Colony Clustering Algorithm based on Information Entropy

Electrical Load Pattern Grouping Based on Centroid Model With Ant Colony Clustering

Load pattern clustering based on the shape of the electricity consumption is a key tool to provide enhanced knowledge on the nature of the consumption and assist meaningful customer partitioning. This paper presents new developments to group the load patterns using an initial set of centroids specified according to a user-defined centroid model. The original Electrical Pattern Ant Colony Clustering (EPACC) algorithm is illustrated, highlighting its characteristics and parameters, with centroids evolution during the iterative process until stabilization. The EPACC results are compared with those obtained from the classical k-means algorithm to group the representative load patterns taken from a set of non-residential customers in typical weekdays.

Ant colony clustering with fitness perception and pheromone diffusion for community detection in complex networks

Community structure detection in complex networks has been intensively investigated in recent years. In this paper, we propose an adaptive approach based on ant colony clustering to discover communities in a complex network. The focus of the method is the clustering process of an ant colony in a virtual grid, where each ant represents a node in the complex network. During the ant colony search, the method uses a new fitness function to percept local environment and employs a pheromone diffusion model as a global information feedback mechanism to realize information exchange among ants. A significant advantage of our method is that the locations in the grid environment and the connections of the complex network structure are simultaneously taken into account in ants moving. Experimental results on computer-generated and real-world networks show the capability of our method to successfully detect community structures.

Historical disaster classification method based on ant colony clustering

Concerning the description and simplicity of historical disaster records,a historical disaster classification method based on ant colony clustering was proposed in this paper.The disaster data were normalized by using gray relational analytical approach,and then the levels of historical disasters were divided by the results of ant colony automatic clustering,so as to avoid the arbitrary man-made interference.In comparison with other classification methods in performance,the experimental results show that this method has higher precision and practicality.

Multi-Loading Ant Colony Clustering Algorithm Based On Dynamic Neighborhood

Multi-Loading Ant Colony Clustering Algorithm Based On Dynamic Neighborhood

Improving Web Content Management System: Template personalization approach

Web Content Management System (WCMS) has been developed to facilitate different aspects of website development. However, up until now, designing of web pages is accomplished by the website developers. Even though WCMS might suggest a number of templates, but it does not consider the nature of the website that will be developed. Many designers have complained about designing the right structure, and different users have complained about poor website structure. Thus, this manuscript will present a new approach that extracts personalized templates by applying web personalization in order to facilitate WCMS and increase its scalability and flexibility. In specific, a new technique that mixes hybrid Evolutionary Algorithm (Genetic Algorithm and Ant Colony Clustering) with Cluster Tree Matching is developed. Moreover, an experiment has been conducted to prove the quality of the proposed approach, which confirmed the good quality of the algorithm concerning speed, precision, and accuracy. Key words: Web personalization, Web Content Management System, group profiling, evolutionary algorithm, document object model.

A Clustering Routing Algorithm Based Ant Colony Optimization for Wireless Sensor Network

As ant colony optimization algorithm and clustering routing algorithm were discussed deeply, a clustering routing algorithm based ant colony optimization (CRAACO) for wireless sensor networks has been put forward. To test the performance of CRAACO, simulations have been done from information fusion rates, remaining energy and network lifetime. The experiment results show that the CRAACO can work effectively and may be used in wireless sensor networks.

Ant colony clustering analysis based intelligent fault diagnosis method and its application to rotating machinery

Fault diagnosis is crucial to improve reliability and performance of machinery. Effective feature extraction and clustering analysis can mine useful information from large amounts of raw data and facilitate fault diagnosis. This paper presents a novel intelligent fault diagnosis method based on ant colony clustering analysis. Vibration signals acquired from equipment are decomposed by wavelet packet transform, after which sub-bands of signals are clustered by ant colony algorithm, and each cluster as a set of data is analyzed from pattern of frequency band perspective for selecting intrinsic features reflecting operation condition of equipment, and thus fault diagnosis model is established to combine the extracted major features with given fault prototypes from historical data. The classification process for fault diagnosis is carried out using Euclidean nearness degree based on the established model. Furthermore, an improved ant colony clustering algorithm is proposed to adjust comparison probability dynamically and detect outliers. When compared with other clustering algorithms, the algorithm has higher convergence speed to meet requirements of real-time analysis as well as further improvement of accuracy. Finally, effectiveness and feasibility of the proposed method is verified by vibration signals acquired from a rotor test bed.

Ant Colony Clustering Algorithm for Handwritten Arabic Numeral Recognition

This article is based on Ant Colony Clustering Algorithm for Handwritten Arabic Numeral Recognition on an image. Under the precondition of the clustering numbers are known and a adequate treatment process was developed for the image, we carry through cluster analysis to digital image by experiment. The article elaborates on the basic concepts and the algorithm’s principle of Ant Colony Clustering Algorithm. The workflow to the algorithmic flow of Ant Colony Clustering Algorithm will be elaborated in the following chapters. The paper also detailed discuss the implement of the algorithm.

A Clustering Ant Colony Algorithm for the Long-Term Car Pooling Problem

Along with the increase of population and the dispersion of habitation, the use of private cars has been increasing drastically. More and more vehicles on the road have caused significant traffic congestion, noise, and energy waste. Car pooling, which is based on the idea that sets of car owners having the same travel destination share their vehicles, has emerged to be a viable possibility for reducing private car usage around the world. This paper describes a clustering ant colony algorithm for solving the long-term car pooling problem. Computational results are given to show the superiority of the authors’ approach compared with other metaheuristics.

Adaptive Ant Colony Clustering Method Applied to Finding Closely Communicating Community

The investigation of community structures in networks is an important issue in many domains and disciplines. Closely communicating community is different from the traditional community which emphasize particularly on structure or context. Our previous method played more emphasis on the feasibility that ant colony algorithm applied to community detection. However the essence of closely communicating community did not be described clearly. In this paper, the definition of closely communicating community is put forward firstly, the four features are described and corresponding methods are introduced to achieve the value of features between each pair. Meanwhile, pair propinquity and local propinquity are put forward and used to guide ants’ decision. Based on the previous work, the closely communicating community detection method is improved in four aspects of adaptive adjusting, which are entropy based weight modulation, combining historical paths and random wandering to select next coordination, the strategy of forcing unloading and the adaptive change of ant’s eyesight. The value selection of parameters is discussed in the portion of experiments, and the results also reveal the improvement of our algorithm in adaptive djusting.

Analysis of Browsing Behaviors with Ant Colony Clustering Algorithm

The characteristics of users' browsing behaviors on websites can be used to analyze system performance as well as network communication, understand users’ reaction and motivation, and build adaptive websites. However, the motivation, requirement and experience of users may dynamically change, which cause difficulty in exactly refining a stable behavior pattern and describing their shifted interest. This paper introduces an optimized ant colony clustering algorithm (OACA) in dynamic pattern discovery, and explores the structured formula to describe the users’ browsing behavior patterns as well as to analyze their characteristics adaptively. The test and results show that users are clustered accurately based on their similar browsing behavior from dynamic Web log data.

Design and Implementation of Airline Customer Segmentation System Based on Ant Colony Clustering Algorithm

Segmentation based on customer value and needs can better guide marketing decision-making of airlines as well as better understand needs of high-value passengers. To address customer segmentation in Customer Relationship Management (CRM), the paper proposed and designed airline customer segmentation system structure based on ant colony clustering. The key ant colony clustering algorithm was also designed and implemented. The ant colony clustering algorithm mainly used adaptively adjusted group similarity to perform clustering and access to initial clustering result. Then all data representation points and abnormal data were inputted into lattice plane scattered randomly. Ant colony algorithm was used for clustering once again and corresponding class label was used to delete abnormal values and obtain complete clusters. Data test example based on ant colony clustering customer analysis platform illustrated its feasibility and effectiveness

An Ant Colony Clustering Algorithm Improved from ATTA

DENNEUBOURG presents the first ant-based clustering algorithm in 1991.Ant colony clustering has the characteristics of automatically clustering, high clustering accuracy, irregular cluster shapes and so on. These are important for a clustering algorithm, so ant colony clustering is arousing more and more data mining researchers’ concentration. In 2004, J. Handl together with his partners normalized the basic ant colony clustering algorithm, getting the ATTA model with superior performance. In this paper, we’ll present an algorithm improved from ATTA which we call it LCA and test LCA on UCI data sets to show its feasibility.