Self-Organized Data and Image Retrieval as a Consequence
of Inter-Dynamic Synergistic Artificial Ant Colonies
39. Vitorino Ramos,
Fernando Muge, Pedro Pina, Self-Organized
Data and Image Retrieval as a
Consequence of Inter-Dynamic Synergistic Relationships in Artificial
Ant Colonies, in Javier Ruiz-del-Solar, Ajith Abraham and Mario
Köppen (Eds.), Frontiers in Artificial Intelligence and
Applications, Soft Computing Systems
- Design, Management and
Applications, 2nd Int. Conf. on Hybrid Intelligent
Systems, IOS
Press, Vol. 87, ISBN 1 5860 32976, pp. 500-509, Santiago, Chile,
Dec.
2002.
a) 
b)
c) 
d)
Figure - From (a) to (d), a sequential clustering task of corpses performed by a real ant colony. In here 1500 corpses are randomly located in a circular arena with radius = 25 cm, where Messor Sancta workers are present. The figure shows the initial state (a), 2 hours (b), 6 hours (c) and 26 hours (d) after the beginning of the experiment (from: Bonabeau E., M. Dorigo, G. Théraulaz. Swarm Intelligence: From Natural to Artificial Systems. Santa Fe Institute in the Sciences of the Complexity, Oxford University Press, New York, Oxford, 1999).
PDF file: paper (449 Kb)
Abstract: Social insects provide us with a powerful metaphor to create decentralized systems of simple interacting, and often mobile, agents. The emergent collective intelligence of social insects – swarm intelligence – resides not in complex individual abilities but rather in networks of interactions that exist among individuals and between individuals and their environment. The study of ant colonies behavior and of their self-organizing capabilities is of interest to knowledge retrieval/ management and decision support systems sciences, because it provides models of distributed adaptive organization which are useful to solve difficult optimization, classification, and distributed control problems, among others. In the present work we overview some models derived from the observation of real ants, emphasizing the role played by stigmergy as distributed communication paradigm, and we present a novel strategy (ACLUSTER) to tackle unsupervised data exploratory analysis as well as data retrieval problems. Moreover and according to our knowledge, this is also the first application of ant systems into digital image retrieval problems. Nevertheless, the present algorithm could be applied to any type of numeric data.
º A. Ghosh, A. Halder, M. Kothari, S. Ghosh, "Aggregation Pheromone Density based Data Clustering", Information Sciences Journal, Volume 178, Issue 13, pp. 2816-2831, 2008.
º M.S. Baghshah, S.B. Shouraki, C. Lucas, "An Agent-based Clustering Algorithm using Potential Fields", in IEEE/ACS Int. Conference on Computer Systems and Applications (AICCS 08), pp. 551-558, IEEE, Doha, April 2008.
º Herrmann, L., Ultsch, A., "Explaining Ant-Based Clustering on the basis of Self-Organizing Maps", in Verleysen M. (Eds), Proc. of the European Symposium on Artificial Neural Networks (ESANN 2008), pp. 215-220, Bruges, Belgium, 2008.
º Cao, H., Huang, P., Luo, S., "A Novel Image Segmentation Algorithm based on Artificial Ant Colonies", in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4987, LNCS, pp. 63-71, Springer, 2008.
º Konstantinidis, K., Sirakoulis, G.C. and Andreadis, I., "An Intelligent Image Retrieval System Based on the Synergy of Color and Artificial Ant Colonies", in SCIA 07, Scandinavian Conference on Image Analysis, Springer LNCS, pp. 868-877, Aalborg, Denmark, 10-14 June 2007.
º Steven Schockaert, Martine De Cock, Chris Cornelis, Etienne E. Kerre , "Clustering Web search results using Fuzzy Ants", in International Journal of Intelligent Systems, Vol. 22, Issue 5 , pp. 455-474, 2007.
º Tom De Wolf, "Analysing and Engineering Self-Organising Emergent Applications", PhD Thesis, Katholieke Universiteit Leuven, Dep. Comp. Science, Leuven, Belgium, May 2007.
º Bart Gilner, "A Comparative Study Of Ant Clustering Algorithms", in Msc Thesis, University of Maastricht, Department of Mathematics, Netherlands, October 2007.
º A. Abraham, Swagatam Das and Sandip Roy, "Swarm Intelligence Algorithms for Data Clustering", in Soft Computing for Knowledge Discovery and Data Mining, Oded Maimon and Lior Rokach (Eds.), Springer Verlag, Germany, 2007.
º Fazel Keshtkar; Wail Gueaieb, "Segmentation of Dental Radiographs Using a Swarm Intelligence Approach", in Canadian Conference on Electrical and Computer Engineering, CCECE 06, pp. 328-331, IEEE Press, 2006.
º Zhang Jianhua, Zhao Dongdong, Jiang He, Zhang Xianchao, "An Ant Colony Clustering Algorithm Based on Pheromone", in Computer Engineering and Applications Journal, Vol. 42, n. 20, pp.157-159, 163, 2006.
º Feng, Y., Zhong, J., Ye, C.-X., Wu, Z.-F., "Clustering based on self-organizing ant colony networks with application to intrusion detection", (2006) Proceedings - ISDA 06: Sixth International Conference on Intelligent Systems Design and Applications, 2, art. no. 4021813, pp. 1077-1080, 2006.
º Dmitriy Iourinskiy, Scott A. Starks, Vladik Kreinovich, and Stephen F. Smith, "Swarm Intelligence: Theoretical Proof that Empirical Techniques are Optimal", full chapter in A. Abraham, C. Grosan, and V. Ramos (Eds.), Stigmergic Optimization, Springer-Verlag, Berlin-Heidelberg, 2006.
º Claus Aranha and Hitoshi Iba, "The Effect of Using Evolutionary Algorithms on Ant Clustering Techniques", Proceedings of the 2006 Asia Pacific Workshop on Genetic Programming (ASPGP06), pp. 24-34.,2006.
º Zhang Shuren, "Study from Social Software Web 2.0 to Complex Adaptive Information System", Doctoral Dissertation, Renmin University of China, China, June 2006.
º Majid Kazemian, Yoosef Ramezani, Caro Lucas and Behzad Moshiri, "Swarm Clustering Based on Flowers Pollination by Artificial Bees", in Abraham, Grosan, Ramos (Eds.), Swarm Intelligence in Data Mining, Studies in Computational Intelligence series, Vol. 34, pp. 191-201, Springer, Heidelberg, 2006.
º C. Aranha, H. Iba, "Using Genetic Algorithms to improve Ant Colony Clustering", MPS 06, Japan, 2006.
º Kim In-Kyeom, Yun Min-Young, "Improved Edge Detection Algorithm Using Ant Colony System", in The KIPS transactions. Part B, Volume b13, Issue 3, pp. 315-322, Korea Information Processing Society, June 2006.
º Claus de Castro Aranha; "A Survey on using Ant-Based techniques for clustering", survey paper, IBA institute's seminar, IBA Lab. - Research Laboratory of Genetic and Evolutionary Computations (GEC) of the Graduate School of Frontier Sciences, The University of Tokyo, Japan, 20 Jan. 2006.
º Sanabria Garzón Jhon Alexis, "Inteligencia de Enjambre y Lógica Difusa Aplicada a la Minería Web: Importancia, Estado del Arte", Web Mining survey, Colombia, 2006.
º Chen Yun-fei, Liu Yu-shu, Qian Yue-ying et al, "A Heuristic Density-Based Clustering Algorithm of Swarm Intelligence", Transactions of Beijing Institute of Technology, Vol. 25, nº 1, p.45, China, 2005.
º A. Vizine, L.N. de Castro, E.R. Hruschka, R.R. Gudwin, "Towards Improving Clustering Ants: An Adaptive Clustering Algorithm", Informatica Journal, 29, 2005.
º Wang Shugen Yang Yun Lin Ying Cao Chonghua, "Automatic Classification of Remotely Sensed Images Based on Artificial Ant Colony Algorithm", Computer Engineering and Applications Journal, Vol.41, No.29, pp. 77-80,116, 2005.
º A. Abraham, He Guo, and Hongbo Liu, "Swarm Intelligence: Foundations, Perspectives and Applications", in Swarm Intelligence in Data Mining, A. Abraham, C. Grosan, V. Ramos (Eds.), Studies in Computational Intelligence (series), Springer, Germany, 2006.
º Leandro N. de Castro, Fernando J. Von Zuben (Eds.), Recent Developments in Biologically Inspired Computing, Idea Group Publishing Inc., ISBN 1-59140-312-X, 2005.
º Hossein Nezamabadi-pour,Saeid Saryazdi, Esmat Rashedi,"Edge Detection using Ant Algorithms", in Soft Computing - A Fusion of Foundations, Methodologies and Applications, Springer-Verlag GmbH, August 2005.
º Qingyong Li, Zhiping Shi, Zhongzhi Shi, "Swarm Intelligence Clustering Algorithm based on Attractor",in Procs. ICANNGA 05, 7th Int. Conf. on Adaptive and Natural Computing Algorithms, B.Ribeiro et al. (Eds.), LNCS, Springer-Verlag, March 2005.
º Steven Schockaert, Martine De Cock, Chris Cornelis and Etienne E. Kerre, "Fuzzy Ant Based Clustering", in Marco Dorigo, M. Birattari, C. Blum, et al. (Eds.), Ant Colony, Optimization and Swarm Intelligence, ANTS 04, LNCS, Springer-Verlag GmbH, Vol. 3172, p. 342, Nov. 2004.
º Charles E. White II, Gene A. Tagliarini, Sridhar Narayan; "An Algorithm for Swarm-based Color Image Segmentation", in Proc. IEEE SouthEast Conf., Greensboro, North Carolina, USA, IEEE Press, pp. 84-89, March 26-28 2004.
º Vahid Sherafat, Leandro Nunes de Castro, Eduardo R. Hruschka, "TermitAnt: An Ant Clustering Algorithm Improved by Ideas from Termite Colonies", in Neural Information Processing: 11th International Conference, ICONIP 2004 Proc., Nikhil R. Pal, Nikola Kasabov, Rajani K. Mudi, et al. (Eds.), LNCS, Springer-Verlag Heidelberg, ISBN: 3-540-23931-6, Vol. 3316, pp. 1088, Calcutta, India, November 22-25, 2004.
º S. Schockaert, M. De Cock, C. Cornelis, E. E. Kerre, "Efficient Clustering with Fuzzy Ants", in Applied Computational Intelligence, World Scientific Press, 2004.
º Teemu Ekola, Mikko Laurikkala, Timo Lehto and Hannu Koivisto, "Network Traffic Analysis Using Clustering Ants", in WAC 2004, World Automation Congress, Sevilla, Spain, June 2004.
º Sherafat, V.; De Castro, L. N.; Hruschka, E. R., "The Influence of Pheromone and Adaptive Vision on the Standard Ant Clustering Algorithm", In: L. N. de Castro and Fernando J. Von Zuben. (Eds.). Recent Developments in Biologically Inspired Computing, Idea Group Incorporation (IGI), pp. 207-234, 2004.
º T. Mikami, M. Wada, "Ant-based Construction of RBF Network for Human Face Detection", SICE-2004, Sapporo, Japan, June 2004.
º Ekola, T., Laurikkala, M., Lehto, T. and Koivisto, H.,"Capturing Time varying Characteristics of Network Traffic With Cooperative Ants", in NEW2AN 04 - Next Generation Teletraffic and Wired/Wireless Advanced Networking, St. Petersburg, Russia, February 2004.
º Benjamin Walsham, "Simplified and Optimised Ant Sort for Complex Problems: Document Classification", MSc Thesis, Monash University, Australia, Nov. 2003.
º Walker, R.L., "A framework for high-performance Web Mining in dynamic environment using Honeybee search strategies", in Ajith Abraham, Katrin Franke, Mario Koppen (Ed), Advanced in Soft Computing: Intelligent Systems Design and Applications, Springer Verlag, pp. 193-204. Berlin Heidelberg New York ISBN 3-540-40426-0, 2003.
º "Future Cluster Competitiveness - R&D Inventory and Analysis of Growth Opportunities in the Research Triangle Region", Developed by Research Triangle Institute (RTI International), North Carolina, USA, Sep. 2003.
º Magnus E.H. Pedersen, "Ant Colony Clustering and Sorting", Course project, Dept. of Computer Science, Daimi Faculty of Science, University of Aarhus, Denmark, July 2003.
º Leo Neumann, "Using Pheromone Trails for Agent-based Clustering", MSc Coursework, Adaptive System Project, Evolutionary and Adaptive Systems, Univ. of Sussex, UK, 2002.
º Steven Schockaert, "Het Clusteren van Zoekresultaten met behulp van Vaagmieren", Faculteit Wetenschappen, Vakgroep Toegepaste Wiskunde en Informatica, Gent University, Belgium, May 2004.
º Nicolai Marquardt, "Swarm-Intelligence: Modelle und Anwendungen", Seminar: VR-Technologien, Prof. Dr. B. Fröhlich, Bauhaus-Universität Weimar, Jan. 2004.
º Nicolai Marquardt, "Intelligenz von Schwärmen: Grundlagen, Simulations-Modelle und Anwendungen", Bauhaus-Universität Weimar, Jan. 2004.
º Dylan A. Shell, "An Annotated Bibliography of Papers that make use of the word `Stigmergy´", Univ. of Southern California, USA, Dec. 2003.
Related Works:
a) b) c) d)Figure - From (a) to (d), a sequential clustering task of corpses performed by a real ant colony. In here 1500 corpses are randomly located in a circular arena with radius = 25 cm, where Messor Sancta workers are present. The figure shows the initial state (a), 2 hours (b), 6 hours (c) and 26 hours (d) after the beginning of the experiment (from: Bonabeau E., M. Dorigo, G. Théraulaz. Swarm Intelligence: From Natural to Artificial Systems. Santa Fe Institute in the Sciences of the Complexity, Oxford University Press, New York, Oxford, 1999).
PDF file: paper (449 Kb)
Abstract: Social insects provide us with a powerful metaphor to create decentralized systems of simple interacting, and often mobile, agents. The emergent collective intelligence of social insects – swarm intelligence – resides not in complex individual abilities but rather in networks of interactions that exist among individuals and between individuals and their environment. The study of ant colonies behavior and of their self-organizing capabilities is of interest to knowledge retrieval/ management and decision support systems sciences, because it provides models of distributed adaptive organization which are useful to solve difficult optimization, classification, and distributed control problems, among others. In the present work we overview some models derived from the observation of real ants, emphasizing the role played by stigmergy as distributed communication paradigm, and we present a novel strategy (ACLUSTER) to tackle unsupervised data exploratory analysis as well as data retrieval problems. Moreover and according to our knowledge, this is also the first application of ant systems into digital image retrieval problems. Nevertheless, the present algorithm could be applied to any type of numeric data.
Keywords: Ant Systems, Unsupervised Clustering, Data and Image Retrieval, Data Mining, Distributed Computing, Collective Decision Support Systems, Self-Organization and Stigmergy, Swarm Intelligence, Ant-based Clustering.
Cited by:º A. Ghosh, A. Halder, M. Kothari, S. Ghosh, "Aggregation Pheromone Density based Data Clustering", Information Sciences Journal, Volume 178, Issue 13, pp. 2816-2831, 2008.
º M.S. Baghshah, S.B. Shouraki, C. Lucas, "An Agent-based Clustering Algorithm using Potential Fields", in IEEE/ACS Int. Conference on Computer Systems and Applications (AICCS 08), pp. 551-558, IEEE, Doha, April 2008.
º Herrmann, L., Ultsch, A., "Explaining Ant-Based Clustering on the basis of Self-Organizing Maps", in Verleysen M. (Eds), Proc. of the European Symposium on Artificial Neural Networks (ESANN 2008), pp. 215-220, Bruges, Belgium, 2008.
º Cao, H., Huang, P., Luo, S., "A Novel Image Segmentation Algorithm based on Artificial Ant Colonies", in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4987, LNCS, pp. 63-71, Springer, 2008.
º Guo Hui-Lin, Su Yi-Dan, "New
Ant Clustering algorithm based on Multi-Strategies", in Computer Eng.
and Applications Journal,
Vol. 44, n. 16, pp. 154-156, China, 2008.
º Konstantinidis, K., Sirakoulis, G.C. and Andreadis, I., "An Intelligent Image Retrieval System Based on the Synergy of Color and Artificial Ant Colonies", in SCIA 07, Scandinavian Conference on Image Analysis, Springer LNCS, pp. 868-877, Aalborg, Denmark, 10-14 June 2007.
º Steven Schockaert, Martine De Cock, Chris Cornelis, Etienne E. Kerre , "Clustering Web search results using Fuzzy Ants", in International Journal of Intelligent Systems, Vol. 22, Issue 5 , pp. 455-474, 2007.
º Tom De Wolf, "Analysing and Engineering Self-Organising Emergent Applications", PhD Thesis, Katholieke Universiteit Leuven, Dep. Comp. Science, Leuven, Belgium, May 2007.
º Bart Gilner, "A Comparative Study Of Ant Clustering Algorithms", in Msc Thesis, University of Maastricht, Department of Mathematics, Netherlands, October 2007.
º A. Abraham, Swagatam Das and Sandip Roy, "Swarm Intelligence Algorithms for Data Clustering", in Soft Computing for Knowledge Discovery and Data Mining, Oded Maimon and Lior Rokach (Eds.), Springer Verlag, Germany, 2007.
º Fazel Keshtkar; Wail Gueaieb, "Segmentation of Dental Radiographs Using a Swarm Intelligence Approach", in Canadian Conference on Electrical and Computer Engineering, CCECE 06, pp. 328-331, IEEE Press, 2006.
º Zhang Jianhua, Zhao Dongdong, Jiang He, Zhang Xianchao, "An Ant Colony Clustering Algorithm Based on Pheromone", in Computer Engineering and Applications Journal, Vol. 42, n. 20, pp.157-159, 163, 2006.
º Feng, Y., Zhong, J., Ye, C.-X., Wu, Z.-F., "Clustering based on self-organizing ant colony networks with application to intrusion detection", (2006) Proceedings - ISDA 06: Sixth International Conference on Intelligent Systems Design and Applications, 2, art. no. 4021813, pp. 1077-1080, 2006.
º Dmitriy Iourinskiy, Scott A. Starks, Vladik Kreinovich, and Stephen F. Smith, "Swarm Intelligence: Theoretical Proof that Empirical Techniques are Optimal", full chapter in A. Abraham, C. Grosan, and V. Ramos (Eds.), Stigmergic Optimization, Springer-Verlag, Berlin-Heidelberg, 2006.
º Claus Aranha and Hitoshi Iba, "The Effect of Using Evolutionary Algorithms on Ant Clustering Techniques", Proceedings of the 2006 Asia Pacific Workshop on Genetic Programming (ASPGP06), pp. 24-34.,2006.
º Zhang Shuren, "Study from Social Software Web 2.0 to Complex Adaptive Information System", Doctoral Dissertation, Renmin University of China, China, June 2006.
º Majid Kazemian, Yoosef Ramezani, Caro Lucas and Behzad Moshiri, "Swarm Clustering Based on Flowers Pollination by Artificial Bees", in Abraham, Grosan, Ramos (Eds.), Swarm Intelligence in Data Mining, Studies in Computational Intelligence series, Vol. 34, pp. 191-201, Springer, Heidelberg, 2006.
º C. Aranha, H. Iba, "Using Genetic Algorithms to improve Ant Colony Clustering", MPS 06, Japan, 2006.
º Kim In-Kyeom, Yun Min-Young, "Improved Edge Detection Algorithm Using Ant Colony System", in The KIPS transactions. Part B, Volume b13, Issue 3, pp. 315-322, Korea Information Processing Society, June 2006.
º Claus de Castro Aranha; "A Survey on using Ant-Based techniques for clustering", survey paper, IBA institute's seminar, IBA Lab. - Research Laboratory of Genetic and Evolutionary Computations (GEC) of the Graduate School of Frontier Sciences, The University of Tokyo, Japan, 20 Jan. 2006.
º Sanabria Garzón Jhon Alexis, "Inteligencia de Enjambre y Lógica Difusa Aplicada a la Minería Web: Importancia, Estado del Arte", Web Mining survey, Colombia, 2006.
º Chen Yun-fei, Liu Yu-shu, Qian Yue-ying et al, "A Heuristic Density-Based Clustering Algorithm of Swarm Intelligence", Transactions of Beijing Institute of Technology, Vol. 25, nº 1, p.45, China, 2005.
º A. Vizine, L.N. de Castro, E.R. Hruschka, R.R. Gudwin, "Towards Improving Clustering Ants: An Adaptive Clustering Algorithm", Informatica Journal, 29, 2005.
º Wang Shugen Yang Yun Lin Ying Cao Chonghua, "Automatic Classification of Remotely Sensed Images Based on Artificial Ant Colony Algorithm", Computer Engineering and Applications Journal, Vol.41, No.29, pp. 77-80,116, 2005.
º A. Abraham, He Guo, and Hongbo Liu, "Swarm Intelligence: Foundations, Perspectives and Applications", in Swarm Intelligence in Data Mining, A. Abraham, C. Grosan, V. Ramos (Eds.), Studies in Computational Intelligence (series), Springer, Germany, 2006.
º Leandro N. de Castro, Fernando J. Von Zuben (Eds.), Recent Developments in Biologically Inspired Computing, Idea Group Publishing Inc., ISBN 1-59140-312-X, 2005.
º Hossein Nezamabadi-pour,Saeid Saryazdi, Esmat Rashedi,"Edge Detection using Ant Algorithms", in Soft Computing - A Fusion of Foundations, Methodologies and Applications, Springer-Verlag GmbH, August 2005.
º Qingyong Li, Zhiping Shi, Zhongzhi Shi, "Swarm Intelligence Clustering Algorithm based on Attractor",in Procs. ICANNGA 05, 7th Int. Conf. on Adaptive and Natural Computing Algorithms, B.Ribeiro et al. (Eds.), LNCS, Springer-Verlag, March 2005.
º Steven Schockaert, Martine De Cock, Chris Cornelis and Etienne E. Kerre, "Fuzzy Ant Based Clustering", in Marco Dorigo, M. Birattari, C. Blum, et al. (Eds.), Ant Colony, Optimization and Swarm Intelligence, ANTS 04, LNCS, Springer-Verlag GmbH, Vol. 3172, p. 342, Nov. 2004.
º Charles E. White II, Gene A. Tagliarini, Sridhar Narayan; "An Algorithm for Swarm-based Color Image Segmentation", in Proc. IEEE SouthEast Conf., Greensboro, North Carolina, USA, IEEE Press, pp. 84-89, March 26-28 2004.
º Vahid Sherafat, Leandro Nunes de Castro, Eduardo R. Hruschka, "TermitAnt: An Ant Clustering Algorithm Improved by Ideas from Termite Colonies", in Neural Information Processing: 11th International Conference, ICONIP 2004 Proc., Nikhil R. Pal, Nikola Kasabov, Rajani K. Mudi, et al. (Eds.), LNCS, Springer-Verlag Heidelberg, ISBN: 3-540-23931-6, Vol. 3316, pp. 1088, Calcutta, India, November 22-25, 2004.
º S. Schockaert, M. De Cock, C. Cornelis, E. E. Kerre, "Efficient Clustering with Fuzzy Ants", in Applied Computational Intelligence, World Scientific Press, 2004.
º Teemu Ekola, Mikko Laurikkala, Timo Lehto and Hannu Koivisto, "Network Traffic Analysis Using Clustering Ants", in WAC 2004, World Automation Congress, Sevilla, Spain, June 2004.
º Sherafat, V.; De Castro, L. N.; Hruschka, E. R., "The Influence of Pheromone and Adaptive Vision on the Standard Ant Clustering Algorithm", In: L. N. de Castro and Fernando J. Von Zuben. (Eds.). Recent Developments in Biologically Inspired Computing, Idea Group Incorporation (IGI), pp. 207-234, 2004.
º T. Mikami, M. Wada, "Ant-based Construction of RBF Network for Human Face Detection", SICE-2004, Sapporo, Japan, June 2004.
º Ekola, T., Laurikkala, M., Lehto, T. and Koivisto, H.,"Capturing Time varying Characteristics of Network Traffic With Cooperative Ants", in NEW2AN 04 - Next Generation Teletraffic and Wired/Wireless Advanced Networking, St. Petersburg, Russia, February 2004.
º Benjamin Walsham, "Simplified and Optimised Ant Sort for Complex Problems: Document Classification", MSc Thesis, Monash University, Australia, Nov. 2003.
º Walker, R.L., "A framework for high-performance Web Mining in dynamic environment using Honeybee search strategies", in Ajith Abraham, Katrin Franke, Mario Koppen (Ed), Advanced in Soft Computing: Intelligent Systems Design and Applications, Springer Verlag, pp. 193-204. Berlin Heidelberg New York ISBN 3-540-40426-0, 2003.
º "Future Cluster Competitiveness - R&D Inventory and Analysis of Growth Opportunities in the Research Triangle Region", Developed by Research Triangle Institute (RTI International), North Carolina, USA, Sep. 2003.
º Magnus E.H. Pedersen, "Ant Colony Clustering and Sorting", Course project, Dept. of Computer Science, Daimi Faculty of Science, University of Aarhus, Denmark, July 2003.
º Leo Neumann, "Using Pheromone Trails for Agent-based Clustering", MSc Coursework, Adaptive System Project, Evolutionary and Adaptive Systems, Univ. of Sussex, UK, 2002.
º Steven Schockaert, "Het Clusteren van Zoekresultaten met behulp van Vaagmieren", Faculteit Wetenschappen, Vakgroep Toegepaste Wiskunde en Informatica, Gent University, Belgium, May 2004.
º Nicolai Marquardt, "Swarm-Intelligence: Modelle und Anwendungen", Seminar: VR-Technologien, Prof. Dr. B. Fröhlich, Bauhaus-Universität Weimar, Jan. 2004.
º Nicolai Marquardt, "Intelligenz von Schwärmen: Grundlagen, Simulations-Modelle und Anwendungen", Bauhaus-Universität Weimar, Jan. 2004.
º Dylan A. Shell, "An Annotated Bibliography of Papers that make use of the word `Stigmergy´", Univ. of Southern California, USA, Dec. 2003.
Related Works:
63. Social
Cognitive Maps, Swarm
Collective Perception and Distributed Search on Dynamic Landscapes.
70. Computational
Chemotaxis
in Ants and Bacteria
over Dynamic
Environments.
69. Binary
Ant Algorithm.
55. Exploiting
and Evolving Rn
Mathematical Morphology
Feature
Space.
31. Map
Segmentation by Colour Cube
Genetic K-Mean
Clustering.
51. Evolving a Stigmergic Self-Organized Data-Mining.