Swarm Intelligent Systems
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.Ifoneof theparticlediscovers 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 findingoptimalpaths.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 find food. They return to their colony while laying down pheromone trails. If other ants find such a path, they are likely to follow the trail with some pheromone and deposit more pheromone if they eventually find food.
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Swarm Intelligent Systems
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.Ifoneof theparticlediscovers 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 findingoptimalpaths.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 find food. They return to their colony while laying down pheromone trails. If other ants find such a path, they are likely to follow the trail with some pheromone and deposit more pheromone if they eventually find food.
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Swarm Intelligent Systems

Swarm Intelligent Systems

Swarm Intelligent Systems

Swarm Intelligent Systems

Paperback(Softcover reprint of hardcover 1st ed. 2006)

$169.99 
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Overview

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.Ifoneof theparticlediscovers 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 findingoptimalpaths.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 find food. They return to their colony while laying down pheromone trails. If other ants find such a path, they are likely to follow the trail with some pheromone and deposit more pheromone if they eventually find food.

Product Details

ISBN-13: 9783642070419
Publisher: Springer Berlin Heidelberg
Publication date: 11/23/2010
Series: Studies in Computational Intelligence , #26
Edition description: Softcover reprint of hardcover 1st ed. 2006
Pages: 184
Product dimensions: 6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

Methodologies Based on Particle Swarm Intelligence.- Swarm Intelligence: Foundations, Perspectives and Applications.- Waves of Swarm Particles (WoSP).- Grammatical Swarm: A Variable-Length Particle Swarm Algorithm.- SWARMs of Self-Organizing Polymorphic Agents.- Experiences Using Particle Swarm Intelligence.- Swarm Intelligence — Searchers, Cleaners and Hunters.- Ant Colony Optimisation for Fast Modular Exponentiation using the Sliding Window Method.- Particle Swarm for Fuzzy Models Identification.- A Matlab Implementation of Swarm Intelligence based Methodology for Identification of Optimized Fuzzy Models.
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