Swarm Intelligence: From Natural to Artificial Systems
Eric (Postdoctoral Fellow Bonabeau, Postdoctoral Fellow Santa Fe Institute), Eric Bonabeau, Marco Dorigo, Directeur de Recherches Du Fnrs Marco Dorigo, Guy Theraulaz, Marco (Researcher Dorigo, Researcher Free University of Brussels), Guy Théraulaz, Guy (Researcher Theraulaz, Researcher CNRS University Paul Sabatier)
OUP USA, 1999 - Computers - 307 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 behaviour 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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Other editions - View all
ACO algorithms active agents ant colony algorithm ant colony optimization AntNet ants applied architectures army ants artiﬁcial average behavior Bonabeau brick brood building chapter clusters colony Compute connected constraints coordinated deﬁned Deneubourg deposit destination node distance distribution Dorigo dynamics edges efﬁcient example exploration Figure ﬁnd ﬁnding ﬁrst ﬁtness function ﬁxed ﬂexibility food source foraging genetic algorithm given graph graph partitioning HAS-QAP heuristic implemented individual inﬂuence initial interactions iterations microrules modiﬁed module move neighbor nest nestmates obtained optimization packets parameters path patterns pheromone trail prey probabilistic probability problem pucks random randomly reinforcement Reprinted by permission response thresholds robotic systems routing tables Santa Fe Institute Schoonderwoerd selected self-organization 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