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Comparison of Particle Swarm Optimization Variants

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dc.contributor.author Daggubati, Satyanarayana
dc.description.abstract Particle swarm optimization (PSO) is a heuristic global optimization method, which is based on swarm intelligence. It is inspired by the research on the bird and fish flock movement behavior. The algorithm is widely used and can rapidly be implemented with a few parameters to be tuned. In PSO, individuals, referred to as particles, are “flown” through a hyper-dimensional search space. Changes to the position of particles within the search space are based on the social-psychological tendency of individuals to emulate the success of other individuals. The changes to a particle within the swarm are therefore influenced by the experience, or knowledge, of its neighbors. Many different PSO variants have been proposed in the past. This paper describes a few of these variants that have been implemented, and compares them with standard PSO on a number of benchmark functions measuring both the solution quality and execution time. en_US
dc.title Comparison of Particle Swarm Optimization Variants en_US
dc.date.accessioned 2012-11-28T21:50:30Z
dc.date.available 2012-11-28T21:50:30Z
dc.date.issued 2012-11-28
dc.identifier.uri http://hdl.handle.net/10365/22285
dc.date 2012 en_US
dc.thesis.degree Master of Science. en_US
dc.contributor.advisor Ludwig, Simone
dc.contributor.advisor Nygard, Kendall
dc.subject.course Master of Science / Computer Science, College of Science and Mathematics, 2012. en_US

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