Swarm intelligent systems /

Swarm intelligent systems /

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Bibliographic Details
Imprint:	Berlin : Springer-Verlag, ©2006.
Description:	1 online resource (xx, 184 pages) : illustrations.
Language:	English
Series:	Studies in computational intelligence, 1860-949X ; v. 26 Studies in computational intelligence ; v. 26.
Subject:	Swarm intelligence. Computational intelligence. Computational intelligence. Swarm intelligence.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/11068821

Hidden Bibliographic Details
Other authors / contributors:	Nedjah, Nadia. Macedo Mourelle, Luiza de
ISBN:	9783540338697 3540338691 3540338683 9783540338680 1280701854 9781280701856
Notes:	Includes bibliographical references and index. Print version record.
Summary:	This volume offers a wide spectrum of sample works developed in leading research throughout the world about innovative methodologies of swarm intelligence and foundations of engineering swarm intelligent systems as well as applications and interesting experiences using the particle swarm optimisation. Swarm intelligence is an innovative computational way to solve hard problems. In particular, particle swarm optimization, also commonly known as PSO, mimics the behavior of a swarm of insects or a school of fish. If one of the particle discovers a good path to food the rest of the swarm will be able to follow instantly even if they are far away in the swarm. Swarm behavior is modeled by particles in multidimensional space that have two characteristics: a position and a velocity. These particles wander around the hyperspace and remember the best position that they have discovered. They communicate good positions to each other and adjust their own position and velocity based on these good positions.
Other form:	Print version: Swarm intelligent systems. Berlin : Springer-Verlag, ©2006 3540338683 9783540338680
Standard no.:	9783540338680

Description
Summary:	Swarm intelligence is an innovative computational way to solving hard pr- lems. This discipline is inspired by the behavior of social insects such as ?sh schools and bird ?ocks and colonies of ants, termites, bees and wasps. In g- eral, this is done by mimicking the behavior of the biological creatures within their swarms and colonies. Particle swarm optimization, also commonly known as PSO, mimics the behaviorofaswarmofinsectsoraschoolof?sh.Ifoneoftheparticlediscovers a good path to food the rest of the swarm will be able to follow instantly even if they are far away in the swarm. Swarm behavior is modeled by particles in multidimensionalspacethathavetwocharacteristics:apositionandavelocity. Theseparticleswanderaroundthehyperspaceandrememberthebestposition that they have discovered. They communicate good positions to each other and adjust their own position and velocity based on these good positions. The ant colony optimization, commonly known as ACO, is a probabilistic technique for solving computational hard problems which can be reduced to ?ndingoptimalpaths.ACOisinspiredbythebehaviorofantsin?ndingshort paths from the colony nest to the food place. Ants have small brains and bad vision yet they use great search strategy. Initially, real ants wander randomly to ?nd food. They return to their colony while laying down pheromone trails. If other ants ?nd such a path, they are likely to follow the trail with some pheromone and deposit more pheromone if they eventually ?nd food.
Physical Description:	1 online resource (xx, 184 pages) : illustrations.
Bibliography:	Includes bibliographical references and index.
ISBN:	9783540338697 3540338691 3540338683 9783540338680 1280701854 9781280701856
ISSN:	1860-949X ;