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 assh schools and birdocks 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 assh schools and birdocks 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.
169.99
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Swarm Intelligent Systems
184Swarm Intelligent Systems
184Paperback(Softcover reprint of hardcover 1st ed. 2006)
$169.99
169.99
In Stock
Product Details
ISBN-13: | 9783642070419 |
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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) |
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