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Автор: Grafiati

Опубліковано: 14 березня 2023

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1

Gao, Wei. "Improved Ant Colony Clustering Algorithm and Its Performance Study." Computational Intelligence and Neuroscience 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/4835932.

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Анотація:

Clustering analysis is used in many disciplines and applications; it is an important tool that descriptively identifies homogeneous groups of objects based on attribute values. The ant colony clustering algorithm is a swarm-intelligent method used for clustering problems that is inspired by the behavior of ant colonies that cluster their corpses and sort their larvae. A new abstraction ant colony clustering algorithm using a data combination mechanism is proposed to improve the computational efficiency and accuracy of the ant colony clustering algorithm. The abstraction ant colony clustering algorithm is used to cluster benchmark problems, and its performance is compared with the ant colony clustering algorithm and other methods used in existing literature. Based on similar computational difficulties and complexities, the results show that the abstraction ant colony clustering algorithm produces results that are not only more accurate but also more efficiently determined than the ant colony clustering algorithm and the other methods. Thus, the abstraction ant colony clustering algorithm can be used for efficient multivariate data clustering.

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2

Wei, Li Feng. "Design and Implementation of Airline Customer Segmentation System Based on Ant Colony Clustering Algorithm." Advanced Materials Research 433-440 (January 2012): 3357–61. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3357.

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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

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3

Shelokar, P. S., V. K. Jayaraman, and B. D. Kulkarni. "An ant colony approach for clustering." Analytica Chimica Acta 509, no. 2 (May 2004): 187–95. http://dx.doi.org/10.1016/j.aca.2003.12.032.

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4

İnkaya, Tülin, Sinan Kayalıgil, and Nur Evin Özdemirel. "Ant Colony Optimization based clustering methodology." Applied Soft Computing 28 (March 2015): 301–11. http://dx.doi.org/10.1016/j.asoc.2014.11.060.

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5

Girsang, Abba Suganda, Tjeng Wawan Cenggoro, and Ko-Wei Huang. "Fast Ant Colony Optimization for Clustering." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 1 (October 1, 2018): 78. http://dx.doi.org/10.11591/ijeecs.v12.i1.pp78-86.

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Анотація:

<p>Data clustering is popular data analysis approaches, which used to organizing data into sensible clusters based on similarity measure, where data within a cluster are similar to each other but dissimilar to that of another cluster. In the recently, the cluster problem has been proven as NP-hard problem, thus, it can be solved with meta-heuristic algorithms, such as the particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimization (ACO), respectively. This paper proposes an algorithm called Fast Ant Colony Optimization for Clustering (FACOC) to reduce the computation time of Ant Colony Optimization (ACO) in clustering problem. FACOC is developed by the motivation that a redundant computation is occurred in ACO for clustering. This redundant computation can be cut in order to reduce the computation time of ACO for clustering. The proposed FACOC algorithm was verified on 5 well-known benchmarks. Experimental result shows that by cutting this redundant computation, the computation time can be reduced about 28% while only suffering a small quality degradation.</p>

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6

Runkler, Thomas A. "Ant colony optimization of clustering models." International Journal of Intelligent Systems 20, no. 12 (2005): 1233–51. http://dx.doi.org/10.1002/int.20111.

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7

Hu, Kai Hua, Bing Xiang Liu, and Yu Jing Zhang. "Ant Colony Clustering Algorithm for Handwritten Arabic Numeral Recognition." Applied Mechanics and Materials 190-191 (July 2012): 261–64. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.261.

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Анотація:

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.

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8

Mesleh, Abdelwadood. "Battery Power Clustering Using Ant Colony Optimization." International Journal on Communications Antenna and Propagation (IRECAP) 8, no. 1 (February 28, 2018): 62. http://dx.doi.org/10.15866/irecap.v8i1.13838.

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9

Grace Mercy, M., A. Kamala Kumari, A. Bhujangarao, and V. Nooka Raju. "Ant Colony Optimization Algorithm GPS Clustering Approach." Journal of Physics: Conference Series 2040, no. 1 (October 1, 2021): 012011. http://dx.doi.org/10.1088/1742-6596/2040/1/012011.

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Abstract The geometry of the GPS satellite recipient (s), which reflects the recipient (s) of the satellites, has a major influence on the total positioning precision. The more precise the position, the stronger the geometry of the satellite. This article provides the geometry of satellite clustering for the selection of suitable satellite navigation subsets. This technique is based on the GDOP (Geometric Precision Dilution) satellite factor cluster with the Ant Colony Optimization (ACO) algorithm that has been created by simulating real and artificial ways to locate the quickest route between nesting resources and food. Pheromones are utilised in the suggested technique to assess the iterative outcome of single colonies. The ACO method can measure all subsets of satellites while reducing computer load by eliminating the need for a matrix inversion. Based on the simulation results, the GPS GDOP clustering technique is more efficient at achieving its optimum value.

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10

Klapka, Ondrej, and Adrea Vokalova. "Ant Colony Optimization Method for Graph Clustering." Journal of Engineering and Applied Sciences 14, no. 6 (October 5, 2019): 9289–93. http://dx.doi.org/10.36478/jeasci.2019.9289.9293.

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11

Akarsu, Emre, and Adem Karahoca. "Simultaneous feature selection and ant colony clustering." Procedia Computer Science 3 (2011): 1432–38. http://dx.doi.org/10.1016/j.procs.2011.01.026.

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12

Menéndez, Héctor D., Fernando E. B. Otero, and David Camacho. "Medoid-based clustering using ant colony optimization." Swarm Intelligence 10, no. 2 (May 9, 2016): 123–45. http://dx.doi.org/10.1007/s11721-016-0122-5.

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13

Mandala, Supreet Reddy, Soundar R. T. Kumara, Calyampudi Radhakrishna Rao, and Reka Albert. "Clustering social networks using ant colony optimization." Operational Research 13, no. 1 (May 27, 2011): 47–65. http://dx.doi.org/10.1007/s12351-011-0115-5.

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14

Fang, Jiajuan. "Clustering and Path Planning for Wireless Sensor Networks Based on Improved Ant Colony Algorithm." International Journal of Online and Biomedical Engineering (iJOE) 15, no. 01 (January 17, 2019): 129. http://dx.doi.org/10.3991/ijoe.v15i01.9784.

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To make up the deficiency of artificial intelligent ant colony algorithm in solving the clustering and path planning of wireless sensor network (WSN) a new random disturbance factor is proposed. A self-regulated random disturbance ant colony algorithm is obtained. An improved ant colony algorithm is proposed by combining the self-regulated random disturbance ant colony algorithm with chaos. After the algorithm improvement is completed, the improved artificial intelligent ant colony algorithm is applied to the cluster head fixed WSN node cluster and the path optimization process of each cluster head communication with the base station. The convergence speed, energy consumption and the survival time of the node cluster head are analyzed. The results show that the improved ant colony algorithm has good stability characteristics in the application and convergence of WSN. It can be seen that the improved ant colony algorithm is feasible in clustering and path planning of WSN.

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15

Deng, Lijuan, Long Wan, and Jian Guo. "Research on Security Anomaly Detection for Big Data Platforms Based on Quantum Optimization Clustering." Mathematical Problems in Engineering 2022 (August 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/4805035.

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Анотація:

Due to the explosive growth of data in the Internet, more and more applications are being deployed on Big Data platforms. However, as the scale of data continues to increase, the probability of anomalies in the platform is also increasing. However, traditional anomaly detection techniques cannot effectively handle the massive amount of historical data and can hardly meet the security requirements of big data platforms. In order to solve the above problems, this paper proposes a security anomaly detection method for big data platforms based on quantum optimization clustering. Firstly, a framework of big data platform anomaly detection system is designed based on distributed software architecture through Hadoop and Spark big data open source technology. The system achieves effective detection of network anomalies by collecting and analyzing big data platform server log data. Secondly, an offline anomaly detection algorithm based on quantum ant colony optimized affinity propagation clustering is designed for various anomalies mined from historical data. The bias parameters of the affinity propagation clustering are treated as individual ants to construct an ant colony, and the clustering accuracy is set as fitness. Finally, in order to improve the accuracy of the optimal path search of the ant colony, quantum bit encoding is applied to the ant colony position to refine the granularity of the individual ant colony position update. The experimental results show that the proposed method can effectively complete the anomaly clustering detection of massive data. With a reasonable threshold, the quantum ant colony–based affinity propagation clustering has high detection accuracy.

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16

Guo, En Te, Ting Wu, Li Bing Zhang, and Feng Li Huang. "Study on the Path Optimized Method Based on an Improved Clustering Ant Colony Algorithm for CNC Laser Drilling." Applied Mechanics and Materials 556-562 (May 2014): 4439–42. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4439.

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In order to improve the machining efficiency of the CNC laser drilling, this paper presents the path optimized method based on an improved ant colony algorithm of the k-means clustering approach. The mathematical model of the path optimization is constructed, and the path optimized method based on the improved clustering ant colony algorithm is designed and actualized. The optimized path method for CNC laser drilling based on the improved clustering ant colony algorithm is tested, and the simulative and experimental result have shown that the proposed method is better performance, and the machining efficiency is greatly improved.

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17

Song, Chun Feng, Yuan Bin Hou, and Jing Yi Du. "The Prediction of Grounding Grid Corrosion Rate Using Optimized RBF Network." Applied Mechanics and Materials 596 (July 2014): 245–50. http://dx.doi.org/10.4028/www.scientific.net/amm.596.245.

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Because the grounding grid corrosion rate has the property of nonlinearity and uncertainty, it is very difficult for us to predict precisely. The approach is proposed that ant colony clustering algorithm is combined with RBF neural network to predict the grounding grid corrosion rate, using ant colony clustering algorithm to get the center of hidden layer neurons. To find the best clustering result, local search is applied in ant colony algorithm. This model has good performance of strong local generalization abilities and satisfying accuracy. At last, it is proved with lots of experiments that the application is fairly effective.

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18

Kaveti, Kiran Kumar, and S. Suresh Babu. "Advanced Clustering Specification using ACO (Ant Colony Optimization)." International Journal of Software Engineering for Smart Device 2, no. 1 (April 30, 2015): 1–6. http://dx.doi.org/10.21742/ijsesd.2015.2.1.01.

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19

Ai-Zhong, Mi, and Gong Zhan-Yi. "Ant Colony Clustering Algorithm based on Information Entropy." Information Technology Journal 12, no. 14 (July 1, 2013): 2873–77. http://dx.doi.org/10.3923/itj.2013.2873.2877.

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20

Nithya, S., and R. Manavalan. "An Ant Colony Clustering Algorithm Using Fuzzy Logic." International Journal of Soft Computing and Software Engineering 2, no. 5 (May 19, 2012): 13–24. http://dx.doi.org/10.7321/jscse.v2.n5.2.

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21

Subekti, R., E. R. Sari, and R. Kusumawati. "Ant colony algorithm for clustering in portfolio optimization." Journal of Physics: Conference Series 983 (March 2018): 012096. http://dx.doi.org/10.1088/1742-6596/983/1/012096.

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22

Kao, Y., and Y. L. Li. "Ant colony recognition systems for part clustering problems." International Journal of Production Research 46, no. 15 (August 2008): 4237–58. http://dx.doi.org/10.1080/00207540601078054.

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23

Wang, Jinbiao, Ailing Tu, and Hongwei Huang. "An Ant Colony Clustering Algorithm Improved from ATTA." Physics Procedia 24 (2012): 1414–21. http://dx.doi.org/10.1016/j.phpro.2012.02.210.

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24

Jenson, T., and A. S. Girsang. "Performance of news clustering using ant colony optimization." Journal of Physics: Conference Series 1566 (June 2020): 012101. http://dx.doi.org/10.1088/1742-6596/1566/1/012101.

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25

Nath Sinha, Amarendra, Nibedita Das, and Gadadhar Sahoo. "Ant colony based hybrid optimization for data clustering." Kybernetes 36, no. 2 (February 20, 2007): 175–91. http://dx.doi.org/10.1108/03684920710741215.

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26

Zhao, De Bin, and Ji Hong Yan. "A Novel Feature Extraction Method Using Ant Colony Clustering Analysis." Applied Mechanics and Materials 37-38 (November 2010): 32–35. http://dx.doi.org/10.4028/www.scientific.net/amm.37-38.32.

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A novel feature extraction method is presented by combining wavelet packet transform with ant colony clustering analysis in this paper. Vibration signals acquired from equipments are decomposed by wavelet packet transform, after which frequency bands of signals are clustered by ant colony algorithm, and each cluster as a set of data is analyzed in frequency-domain for extracting intrinsic features reflecting operating condition of machinery. Furthermore, the robust ant colony clustering algorithm is proposed by adjusting comparing probability dynamically. Finally, effectiveness and feasibility of the proposed method are verified by vibration signals acquired from a rotor test bed.

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27

Gao Sun, Mei Lin, Ping Wu, Kai Li, and Zhen Hua Gen. "Application of Ant Colony Algorithm in Plant Leaves Classification Based on Infrared Spectroscopy Analysis." Key Engineering Materials 633 (November 2014): 503–6. http://dx.doi.org/10.4028/www.scientific.net/kem.633.503.

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Intelligent classification is realized according to different components of featured information included in near infrared spectrum data of plants. The core of this theory is to research applications of ant colony algorithm in spectral analysis of plant leaves through theories and experiments. In aspect of theoretical exploration, the built-in function of clustering algorithm is used to compress and process data. In aspect of experimental research, the near infrared diffuse emission spectrum curves of the leaves of Cinnamomum camphora and Acer saccharum Marsh in two groups, which have 75 leaves respectively. Then, the obtained data are processed using ant colony algorithm and the same leaves can be classified as a class by ant colony clustering algorithm. Finally, the two groups of data are classified into two classes. Our results show the distinguishability can be 100%. Keywords:Near infrared spectroscopy; ant colony algorithm; clustering algorithm; signal processing

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28

Chiou, Yu-Chiun, and Shih-Ta Chou. "Ant Custering Algorithms." International Journal of Applied Evolutionary Computation 1, no. 1 (January 2010): 1–15. http://dx.doi.org/10.4018/jaec.2010010101.

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This paper proposes three ant clustering algorithms (ACAs): ACA-1, ACA-2 and ACA-3. The core logic of the proposed ACAs is to modify the ant colony metaheuristic by reformulating the clustering problem into a network problem. For a clustering problem of N objects and K clusters, a fully connected network of N nodes is formed with link costs, representing the dissimilarity of any two nodes it connects. K ants are then to collect their own nodes according to the link costs and following the pheromone trail laid by previous ants. The proposed three ACAs have been validated on a small-scale problem solved by a total enumeration method. The solution effectiveness at different problem scales consistently shows that ACA-2 outperforms among these three ACAs. A further comparison of ACA-2 with other commonly used clustering methods, including agglomerative hierarchy clustering algorithm (AHCA), K-means algorithm (KMA) and genetic clustering algorithm (GCA), shows that ACA-2 significantly outperforms them in solution effectiveness for the most of cases and also performs considerably better in solution stability as the problem scales or the number of clusters gets larger.

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29

Chen, Xian Yi, Zhi Gang Jin, and Xiong Yang. "A Clustering Routing Algorithm Based Ant Colony Optimization for Wireless Sensor Network." Applied Mechanics and Materials 236-237 (November 2012): 1085–89. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.1085.

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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.

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30

Parvin, Hamid, Iman Jafari, and Farhad Rad. "A clustering ensemble learning method based on the ant colony clustering algorithm." International Journal of Innovative Computing and Applications 8, no. 3 (2017): 172. http://dx.doi.org/10.1504/ijica.2017.086637.

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31

Rad, Farhad, Iman Jafari, and Hamid Parvin. "A clustering ensemble learning method based on the ant colony clustering algorithm." International Journal of Innovative Computing and Applications 8, no. 3 (2017): 172. http://dx.doi.org/10.1504/ijica.2017.10007642.

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32

Lan, Yu, Yan Bo, and Yao Baozhen. "Core Business Selection Based on Ant Colony Clustering Algorithm." Mathematical Problems in Engineering 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/136753.

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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.

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33

JIA, Zhi-juan, Ming-sheng HU, and Si LIU. "Historical disaster classification method based on ant colony clustering." Journal of Computer Applications 32, no. 4 (April 8, 2013): 1030–32. http://dx.doi.org/10.3724/sp.j.1087.2012.01030.

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34

Juang, Chia-Feng, Yu-Ping Kang, and Chiang Lo. "Fuzzy Controller Design by Clustering-Aided Ant Colony Optimization." IFAC Proceedings Volumes 41, no. 2 (2008): 12297–302. http://dx.doi.org/10.3182/20080706-5-kr-1001.02082.

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35

Lei Jiang, Datong Xie, and Shenweng Wang. "An Improved Ant Colony Optimization Approach to Automatic Clustering." International Journal of Advancements in Computing Technology 5, no. 8 (April 30, 2013): 222–32. http://dx.doi.org/10.4156/ijact.vol5.issue8.25.

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36

Chiang, Chuan-Wen. "ANT COLONY OPTIMIZATION FOR VLSI FLOORPLANNING WITH CLUSTERING CONSTRAINTS." Journal of the Chinese Institute of Industrial Engineers 26, no. 6 (January 2009): 440–48. http://dx.doi.org/10.1080/10170660909509158.

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37

BURSA, MIROSLAV, and LENKA LHOTSKA. "ARTIFICIAL INTELLIGENCE METHODS IN ELECTROCARDIOGRAM AND ELECTROENCEPHALOGRAM DATA CLUSTERING." International Journal of Computational Intelligence and Applications 08, no. 01 (March 2009): 69–84. http://dx.doi.org/10.1142/s1469026809002448.

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Анотація:

The paper focuses on the field of artificial intelligence techniques and their use in biomedical data processing. It concerns the clustering techniques inspired by various ant colonies. The behavior of ant colonies shows many interesting properties that have been used in static and dynamic combinatorial problem-solving tasks (mostly since 1990). Also applications to data clustering have been proposed. This branch is a subject of ongoing research. After the introduction into the state-of-the-art of ant-colony-inspired metaheuristics, an overview of ant-colony-inspired clustering metaheuristics is presented, together with the ACO_DTree method, developed by the first author, which is based on the autocatalytic collective behavior of real insect colonies. Over the basic algorithm it involves techniques to increase robustness and performance of the method. Application to electrocardiogram and electroencephalogram data processing is also presented, together with comparison to other clustering methods.

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38

Yang, Lei, Xin Hu, Hui Wang, Wensheng Zhang, Kang Huang, and Dongya Wang. "An ACO-Based Clustering Algorithm With Chaotic Function Mapping." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (October 1, 2021): 1–21. http://dx.doi.org/10.4018/ijcini.20211001.oa20.

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Анотація:

To overcome shortcomings when the ant colony optimization clustering algorithm (ACOC) deal with the clustering problem, this paper introduces a novel ant colony optimization clustering algorithm with chaos. The main idea of the algorithm is to apply the chaotic mapping function in the two stages of ant colony optimization: pheromone initialization and pheromone update. The application of chaotic mapping function in the pheromone initialization phase can encourage ants to be distributed in as many different initial states as possible. Applying the chaotic mapping function in the pheromone update stage can add disturbance factors to the algorithm, prompting the ants to explore new paths more, avoiding premature convergence and premature convergence to suboptimal solutions. Extensive experiments on the traditional and proposed algorithms on four widely used benchmarks are conducted to investigate the performance of the new algorithm. These experiments results demonstrate the competitive efficiency, effectiveness, and stability of the proposed algorithm.

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39

Srinivasan, Thenmozhi, and Balasubramanie Palanisamy. "Scalable Clustering of High-Dimensional Data Technique Using SPCM with Ant Colony Optimization Intelligence." Scientific World Journal 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/107650.

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Анотація:

Clusters of high-dimensional data techniques are emerging, according to data noisy and poor quality challenges. This paper has been developed to cluster data using high-dimensional similarity based PCM (SPCM), with ant colony optimization intelligence which is effective in clustering nonspatial data without getting knowledge about cluster number from the user. The PCM becomes similarity based by using mountain method with it. Though this is efficient clustering, it is checked for optimization using ant colony algorithm with swarm intelligence. Thus the scalable clustering technique is obtained and the evaluation results are checked with synthetic datasets.

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40

Diaf, Moussa, Kamal Hammouche, and Patrick Siarry. "From the Real Ant to the Artificial Ant." International Journal of Signs and Semiotic Systems 2, no. 2 (July 2012): 45–68. http://dx.doi.org/10.4018/ijsss.2012070103.

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Анотація:

Biological studies highlighting the collective behavior of ants in fulfilling various tasks by using their complex indirect communication process have constituted the starting point for many physical systems and various ant colony algorithms. Each ant colony is considered as a superorganism which operates as a unified entity made up of simple agents. These agents (ants) interact locally with one another and with their environment, particularly in finding the shortest path from the nest to food sources without any centralized control dictating the behavior of individual agents. It is this coordination mechanism that has inspired researchers to develop plenty of metaheuristic algorithms in order to find good solutions for NP-hard combinatorial optimization problems. In this article, the authors give a biological description of these fascinating insects and their complex indirect communication process. From this rich source of inspiration for researchers, the authors show how, through the real ant, artificial ant is modeled and applied in combinatorial optimization, data clustering, collective robotics, and image processing.

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41

Yao, Ya Chuan, and Yi Yao. "The Application of Ant Colony Optimization in Wireless Sensor Network Routing." Advanced Materials Research 655-657 (January 2013): 838–41. http://dx.doi.org/10.4028/www.scientific.net/amr.655-657.838.

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Анотація:

The paper proposes wireless sensor clustering routing protocol based on ant colony algorithm(ACO). The method firstly uses the network self-topology to get a stable cluster structure based on modularity clustering algorithm; secondly make a new cluster-head selection function according to each cluster node residual energy and the distribution of cluster node; then we propose inter-cluster multi-hop routing algorithm based on ant colony optimization, which in order to balance the energy between consumption on the transmission path and residual one of cluster-head node, developing new ant transmission probability and pheromone updating strategy, we verify the validity of algorithm through simulation.

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42

Veloz, Alejandro, Alejandro Weinstein, Stefan Pszczolkowski, Luis Hernández-García, Rodrigo Olivares, Roberto Muñoz, and Carla Taramasco. "Ant Colony Clustering for ROI Identification in Functional Magnetic Resonance Imaging." Computational Intelligence and Neuroscience 2019 (December 26, 2019): 1–9. http://dx.doi.org/10.1155/2019/5259643.

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Brain network analysis using functional magnetic resonance imaging (fMRI) is a widely used technique. The first step of brain network analysis in fMRI is to detect regions of interest (ROIs). The signals from these ROIs are then used to evaluate neural networks and quantify neuronal dynamics. The two main methods to identify ROIs are based on brain atlas registration and clustering. This work proposes a bioinspired method that combines both paradigms. The method, dubbed HAnt, consists of an anatomical clustering of the signal followed by an ant clustering step. The method is evaluated empirically in both in silico and in vivo experiments. The results show a significantly better performance of the proposed approach compared to other brain parcellations obtained using purely clustering-based strategies or atlas-based parcellations.

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43

Fahy, Conor, Shengxiang Yang, and Mario Gongora. "Ant Colony Stream Clustering: A Fast Density Clustering Algorithm for Dynamic Data Streams." IEEE Transactions on Cybernetics 49, no. 6 (June 2019): 2215–28. http://dx.doi.org/10.1109/tcyb.2018.2822552.

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44

Lu, Mingli, Di Wu, Yuchen Jin, Jian Shi, Benlian Xu, Jinliang Cong, Yingying Ma, and Jiadi Lu. "A Novel Gaussian Ant Colony Algorithm for Clustering Cell Tracking." Discrete Dynamics in Nature and Society 2021 (September 24, 2021): 1–15. http://dx.doi.org/10.1155/2021/9205604.

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Cell behavior analysis is a fundamental process in cell biology to obtain the correlation between many diseases and abnormal cell behavior. Moreover, accurate number estimation plays an important role for the construction of cell lineage trees. In this paper, a novel Gaussian ant colony algorithm, for clustering or spatial overlap cell state and number estimator, simultaneously, is proposed. We have introduced a novel definition of the Gaussian ant system borrowed from the concept of the multi-Bernoulli random finite set (RFS) in the way that it encourages ants searching for cell regions effectively. The existence probability of ant colonies is considered for the number and state estimation of cells. Through experiments on two real cell sequences, it is confirmed that our proposed algorithm could automatically track clustering cells in various scenarios and has enabled superior performance compared with other state-of-the-art approaches.

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45

Zhang, Jun Ye, and Dong Ya Chen. "Clustering Routing Algorithm Ant Colony Optimization-Based for Wireless Sensor Network." Applied Mechanics and Materials 568-570 (June 2014): 594–97. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.594.

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Анотація:

Nodes in wireless sensor network have limited power supply and wireless channels between them are sensitive to interference. In order to make good use of the limited energy, a routing algorithm is proposed which uses the Ant Colony Optimization Algorithm to balance the load of the network and extend the network life, the proposed algorithm utilizes the dynamic adaptability and optimization capabilities of the ant colony to get the optimum route between the cluster heads．Simulation results show the feasibility and effectiveness of this algorithm．

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46

Hu, Kai-Cheng, Chun-Wei Tsai, Ming-Chao Chiang, and Chu-Sing Yang. "A Multiple Pheromone Table Based Ant Colony Optimization for Clustering." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/158632.

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Ant colony optimization (ACO) is an efficient heuristic algorithm for combinatorial optimization problems, such as clustering. Because the search strategy of ACO is similar to those of other well-known heuristics, the probability of searching particular regions will be increased if better results are found and kept. Although this kind of search strategy may find a better approximate solution, it also has a high probability of losing the potential search directions. To prevent the ACO from losing too many potential search directions at the early iterations, a novel pheromone updating strategy is presented in this paper. In addition to the “original” pheromone table used to keep track of thepromisinginformation, a second pheromone table is added to the proposed algorithm to keep track of theunpromisinginformation so as to increase the probability of searching directions worse than the current solutions. Several well-known clustering datasets are used to evaluate the performance of the proposed method in this paper. The experimental results show that the proposed method can provide better results than ACO and other clustering algorithms in terms of quality.

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47

Sheshathri, V., and S. Sukumaran. "An Enhanced Ant Colony Clustering Method for Color Image Segmentation." Indian Journal of Science and Technology 10, no. 17 (May 1, 2017): 1–7. http://dx.doi.org/10.17485/ijst/2017/v10i17/109509.

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48

Saveetha, V., S. Sophia, and P. D. R. Vijaya Kumar. "GPU Accelerated K Means Clustering Refined using ANT Colony Optimization." Asian Journal of Research in Social Sciences and Humanities 6, no. 10 (2016): 1736. http://dx.doi.org/10.5958/2249-7315.2016.01126.6.

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XianWen LUO. "Multi-Loading Ant Colony Clustering Algorithm Based On Dynamic Neighborhood." International Journal of Advancements in Computing Technology 5, no. 3 (February 15, 2013): 161–69. http://dx.doi.org/10.4156/ijact.vol5.issue3.19.

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50

LI, Hai-fang, Xia WEN, and Li-huan MEN. "Clustering algorithm of image emotional characteristics based on ant colony." Journal of Computer Applications 29, no. 2 (April 7, 2009): 360–63. http://dx.doi.org/10.3724/sp.j.1087.2009.00360.

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