Organic Computing and Swarm Intelligence

The relations between swarm intelligence and organic computing are discussed in this chapter. The aim of organic computing is to design and study computing systems that consist of many autonomous components and show forms of collective behavior. Such organic computing systems (OC systems) should possess self-x properties (e.g., self-healing, self-managing, self-optimizing), have a decentralized control, and be adaptive to changing requirements of their user. Examples of OC systems are described in this chapter and two case studies are presented that show in detail that OC systems share important properties with social insect colonies and how methods of swarm intelligence can be used to solve problems in organic computing.

[1]  Martin Middendorf,et al.  Searching for a new home—scouting behavior of honeybee swarms , 2007 .

[2]  Christian Müller-Schloer,et al.  Emergence in Organic Computing Systems: Discussion of a Controversial Concept , 2006, ATC.

[3]  Nicolas Monmarché,et al.  AntClust: Ant Clustering and Web Usage Mining , 2003, GECCO.

[4]  Julia Handl,et al.  Improved Ant-Based Clustering and Sorting , 2002, PPSN.

[5]  Nicolas Monmarché,et al.  Visual Clustering with Artificial Ants Colonies , 2003, KES.

[6]  Daniel Merkle,et al.  Swarm Controlled Emergence - Designing an Anti-Clustering Ant System , 2007, 2007 IEEE Swarm Intelligence Symposium.

[7]  Christian Müller-Schloer,et al.  Quantitative Emergence , 2006, 2006 IEEE Mountain Workshop on Adaptive and Learning Systems.

[8]  J. Deneubourg,et al.  Dynamics of Aggregation and Emergence of Cooperation , 2002, The Biological Bulletin.

[9]  Dietmar Fey,et al.  An Organic Computing architecture for visual microprocessors based on Marching Pixels , 2007, 2007 IEEE International Symposium on Circuits and Systems.

[10]  Pascale Kuntz,et al.  Emergent colonization and graph partitioning , 1994 .

[11]  Wolfgang Rosenstiel,et al.  Organic Computing at the System on Chip Level , 2006, 2006 IFIP International Conference on Very Large Scale Integration.

[12]  Sándor P. Fekete,et al.  Recognizing Traffic Jams with Hovering Data Clouds , 2006, Second International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (isola 2006).

[13]  Jean-Louis Deneubourg,et al.  Organisation spatiale du matériel provenant de l'excavation du nid chez Messor barbarus et des cadavres d'ouvrières chez Lasius niger (Hymenopterae: Formicidae) , 1996 .

[14]  A. Baddeley,et al.  A non-parametric measure of spatial interaction in point patterns , 1996, Advances in Applied Probability.

[15]  Guy Theraulaz,et al.  Task partitioning in a ponerine ant. , 2002, Journal of theoretical biology.

[16]  Nicolas Monmarché,et al.  A new clustering algorithm based on the chemical recognition system of ants , 2002 .

[17]  Dietmar Fey,et al.  Marching Pixels - Using Organic Computing Principles in Embedded Parallel Hardware , 2006, International Symposium on Parallel Computing in Electrical Engineering (PARELEC'06).

[18]  P.-P. Grasse La reconstruction du nid et les coordinations interindividuelles chezBellicositermes natalensis etCubitermes sp. la théorie de la stigmergie: Essai d'interprétation du comportement des termites constructeurs , 1959, Insectes Sociaux.

[19]  Tucker R. Balch,et al.  Hierarchic Social Entropy: An Information Theoretic Measure of Robot Group Diversity , 2000, Auton. Robots.

[20]  J. Deneubourg,et al.  Dynamics of aggregation in Lasius niger (Formicidae): influence of polyethism , 2004, Insectes Sociaux.

[21]  D. Grünbaum Schooling as a strategy for taxis in a noisy environment , 1998, Evolutionary Ecology.

[22]  J. Boomsma,et al.  Task partitioning in insect societies: bucket brigades , 2002, Insectes Sociaux.

[23]  Christian Müller-Schloer,et al.  Emergence in Technical Systems (Emergenz in technischen Systemen) , 2005, it Inf. Technol..

[24]  Jean-Louis Deneubourg,et al.  The dynamics of collective sorting robot-like ants and ant-like robots , 1991 .

[25]  Ricard V. Solé,et al.  Pattern Formation and Optimization in Army Ant Raids , 2000, Artificial Life.

[26]  J Delgado,et al.  Self-synchronization and task fulfilment in ant colonies. , 2000, Journal of theoretical biology.

[27]  Thorsten Schöler,et al.  An Observer/Controller Architecture for Adaptive Reconfigurable Stacks , 2005, ARCS.

[28]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[29]  E. Bonabeau,et al.  Spatial patterns in ant colonies , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[30]  J. Pasteels,et al.  Response thresholds to recruitment signals and the regulation of foraging intensity in the ant Myrmica sabuleti (Hymenoptera, Formicidae) , 2000, Behavioural Processes.

[31]  Hartmut Schmeck,et al.  Towards a generic observer/controller architecture for Organic Computing , 2006, GI Jahrestagung.

[32]  G. Theraulaz,et al.  Response threshold reinforcements and division of labour in insect societies , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[33]  G B Blanchard,et al.  Gaseous templates in ant nests. , 2000, Journal of theoretical biology.

[34]  N. Franks,et al.  Brood sorting by ants: distributing the workload over the work-surface , 1992, Behavioral Ecology and Sociobiology.

[35]  Jean-Louis Deneubourg,et al.  A Basis for Spatial and Social Patterns in Ant Species: Dynamics and Mechanisms of Aggregation , 2004, Journal of Insect Behavior.

[36]  A. G. Nicolas,et al.  Immediate and Latent Effects of Carbon Dioxide on Insects , 1989 .

[37]  Parag M. Kanade,et al.  Fuzzy ants as a clustering concept , 2003, 22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003.

[38]  Hartmut Schmeck,et al.  An Organic Architecture for Traffic Light Controllers , 2006, GI Jahrestagung.

[39]  Marco Dorigo,et al.  On the Performance of Ant-based Clustering , 2003, HIS.

[40]  Thomas Stützle,et al.  Ant Colony Optimization , 2009, EMO.

[41]  Marco Dorigo,et al.  Strategies for the Increased Robustness of Ant-Based Clustering , 2003, Engineering Self-Organising Systems.

[42]  Ana B Sendova-Franks,et al.  Random walk models of worker sorting in ant colonies. , 2002, Journal of theoretical biology.

[43]  Hartmut Schmeck,et al.  Organic Computing - A New Vision for Distributed Embedded Systems , 2005, ISORC.