Swarm Intelligence : From Natural to Artificial Systems: From Natural to Artificial Systems
Eric Bonabeau Interval Research Fellow Santa Fe Institute, Marco Dorigo Interval Research Fellow Research Associate with the Belgian Fonds National pour la Recherche Scientifique, Guy Theraulaz Research Associate French Centre National de la Recherche Scientifique (CNRS)
Oxford University Press, USA, Aug 27, 1999 - Medical - 320 pages
Social insects--ants, bees, termites, and wasps--can be viewed as powerful problem-solving systems with sophisticated collective intelligence. Composed of simple interacting agents, this intelligence lies in the networks of interactions among individuals and between individuals and the environment. A fascinating subject, social insects are also a powerful metaphor for artificial intelligence, and the problems they solve--finding food, dividing labor among nestmates, building nests, responding to external challenges--have important counterparts in engineering and computer science. This book provides a detailed look at models of social insect behavior and how to apply these models in the design of complex systems. The book shows how these models replace an emphasis on control, preprogramming, and centralization with designs featuring autonomy, emergence, and distributed functioning. These designs are proving immensely flexible and robust, able to adapt quickly to changing environments and to continue functioning even when individual elements fail. In particular, these designs are an exciting approach to the tremendous growth of complexity in software and information. Swarm Intelligence draws on up-to-date research from biology, neuroscience, artificial intelligence, robotics, operations research, and computer graphics, and each chapter is organized around a particular biological example, which is then used to develop an algorithm, a multiagent system, or a group of robots. The book will be an invaluable resource for a broad range of disciplines.
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Ant Foraging Behavior Combinatorial Optimization and Routing in Communications Networks
Division of Labor and Task Allocation
Cemetery Organization Brood Sorting Data Analysis and Graph Partitioning
SelfOrganization and Templates Application to Data Analysis and Graph Partitioning
Nest Building and SelfAssembling
ACO algorithms activities adaptive agents ants applied architectures artificial AS-QAP AS-TSP average bees behavior Bonabeau brick brood building chapter clusters colony complex Computer connected constraints coordinated Deneubourg deposit destination node distance distribution Dorigo dynamics edges example experiments exploration Figure fitness function food source foraging fraction Gambardella genetic algorithm given graph graph partitioning heuristic IEEE implemented individual initial interactions iterations Kube mechanism microrules module move nest nestmates obtained optimization packets parameters path patterns Pheidole pheromone trail prey probabilistic probability pucks Quadratic Assignment Problem random randomly recruitment reinforcement Reprinted by permission response thresholds robotic systems routing tables Santa Fe Institute Schoonderwoerd selected self-assembling self-organization simulated annealing simulations social insects solutions space spatial species steps stigmergy stimuli structure swarm intelligence swarm-based tabu search task performance template termites Theraulaz tour traffic transport Traveling Salesman Problem unit vertices wasps workers
Page vii - Dr. George A. Cowan Visiting Scientist, Santa Fe Institute and Senior Fellow Emeritus, Los Alamos National Laboratory Prof. Marcus W. Feldman Director, Institute for Population & Resource Studies, Stanford University Prof. Murray Gell-Mann Division of Physics & Astronomy, California Institute of Technology Prof.