About: Particle swarm optimization

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In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formula over the particle's position and velocity. Each particle's movement is influenced by its local best known position, but is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions.

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rdf:type	software
rdfs:label	Particle swarm optimization (en)
rdfs:comment	In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formula over the particle's position and velocity. Each particle's movement is influenced by its local best known position, but is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions. (en)
sameAs	Particle swarm optimization Particle swarm optimization
dbp:wikiPageUsesTemplate	dbt:Cite_journal dbt:Commonscat dbt:Reflist dbt:Short_description dbt:Use_American_English dbt:Swarming dbt:Evolutionary_algorithms dbt:Major_subfields_of_optimization
Subject	Evolutionary algorithms Metaheuristics
gold:hypernym	Method
prov:wasDerivedFrom	http://en.wikipedia.org/wiki/Particle_swarm_optimization?oldid=1050772675&ns=0
Wikipage page ID	337083 (xsd:integer)
page length (characters) of wiki page	47636 (xsd:nonNegativeInteger)
Wikipage revision ID	1050772675 (xsd:integer)
has abstract	In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formula over the particle's position and velocity. Each particle's movement is influenced by its local best known position, but is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions. PSO is originally attributed to Kennedy, Eberhart and Shi and was first intended for simulating social behaviour, as a stylized representation of the movement of organisms in a bird flock or fish school. The algorithm was simplified and it was observed to be performing optimization. The book by Kennedy and Eberhart describes many philosophical aspects of PSO and swarm intelligence. An extensive survey of PSO applications is made by Poli. Recently, a comprehensive review on theoretical and experimental works on PSO has been published by Bonyadi and Michalewicz. PSO is a metaheuristic as it makes few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions. Also, PSO does not use the gradient of the problem being optimized, which means PSO does not require that the optimization problem be differentiable as is required by classic optimization methods such as gradient descent and quasi-newton methods. However, metaheuristics such as PSO do not guarantee an optimal solution is ever found. (en)
foaf:isPrimaryTopicOf	http://en.wikipedia.org/wiki/Particle_swarm_optimization
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