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A new optimizer using particle swarm theory

R.C. Eberhart; James Kennedy · OpenAlex · 2002

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Author / responsible party
R.C. Eberhart; James Kennedy
Publisher
OpenAlex
Year
2002
Language
English

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The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed.

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APA 7

Eberhart, R. & Kennedy, J. (2002). A new optimizer using particle swarm theory. https://doi.org/10.1109/mhs.1995.494215

MLA

Eberhart, R.C, and James Kennedy. "A new optimizer using particle swarm theory." 2002. https://doi.org/10.1109/mhs.1995.494215.

Chicago

Eberhart, R.C. and James Kennedy. 2002. "A new optimizer using particle swarm theory.". https://doi.org/10.1109/mhs.1995.494215.

Harvard

Eberhart, R. and Kennedy, J. 2002, A new optimizer using particle swarm theory, OpenAlex, available at: https://doi.org/10.1109/mhs.1995.494215 [Accessed 7 Aug. 2026].

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Title
A new optimizer using particle swarm theory
Author / contributors
R.C. Eberhart; James Kennedy
Publisher
OpenAlex
Publication year
2002
Language
English

Subjects

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Particle swarm optimization; Benchmark (surveying); Computer science; Multi-swarm optimization; Evolutionary computation; Implementation; Artificial neural network; Task (project management); Metaheuristic; Artificial intelligence; Computation; Swarm robotics
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