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Item Details
Title: MULTI-OBJECTIVE SWARM INTELLIGENT SYSTEMS
THEORY & EXPERIENCES
By: Nadia Nedjah (Editor), Leandro Dos S. Coelho (Editor), Luiza de Macedo Mourelle (Editor)
Format: Hardback

List price: £129.99


We currently do not stock this item, please contact the publisher directly for further information.

ISBN 10: 3642051642
ISBN 13: 9783642051647
Publisher: SPRINGER-VERLAG BERLIN AND HEIDELBERG GMBH & CO. KG
Pub. date: 18 November, 2009
Series: Studies in Computational Intelligence 261
Pages: 211
Description: This book covers the latest in multi-objective swarm intelligence and cooperative behavior. It contains innovative and intriguing applications as well as additions to the methodology and theory of genetic programming.
Synopsis: Recently, a new class of heuristic techniques, the swarm intelligence has emerged. In this context, more recently, biologists and computer scientists in the ?eld of"arti?cial life"have been turning to insects for ideas that can be used for heuristics. Many aspects of the collective activities of social insects, such as foraging of ants, birds ?ocking and ?sh schooling are self-organizing, meaning that complex group behavior emerges from the interactions of in- viduals who exhibit simple behaviors by themselves. Swarm intelligence is an innovative computational way to solving hard problems. This discipline is mostly inspired by the behavior of ant colonies, bird ?ocks and ?sh schools and other biological creatures. In general, this is done by mimicking the behavior of these swarms. Swarm intelligence is an emerging research area with similar population and evolution characteristics to those of genetic algorithms. However, it di?erentiates in emphasizing the cooperative behavior among group m- bers. Swarm intelligence is used to solve optimization and cooperative pr- lems among intelligent agents, mainly in arti?cial network training, co- erative and/or decentralized control, operational research, power systems, electro-magnetics device design, mobile robotics, and others.The most we- knownrepresentativesofswarmintelligenceinoptimizationproblemsare:the food-searching behavior of ants, particle swarm optimization, and bacterial colonies. Real-world engineering problems often require concurrent optimization of several design objectives, which are con?icting in most of the cases. Such an optimization is generally called multi-objective or multi-criterion optimi- tion.Inthis context,the developmentofimprovementsfor swarmintelligence methods to multi-objective problems is an emergent research area.
Illustrations: 50 Tables, black and white; XXI, 211 p.
Publication: Germany
Imprint: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Returns: Returnable
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