book · InTech eBooks · 2009

Particle Swarm Optimization

Protozoic Behaviour, Ajith Abraham

Cited by 296Abstract and details only

Abstract

Abstract—Nature inspired algorithms implement successful optimization and adaptation strategies observed in the nature. Various bio-inspired algorithms mimic the behavioural patterns of plants, animals, their communities and their evolution. Surpris-ingly, the behavioural patterns and survival strategies of protozoa, one of the most prevalent and successful species on Earth, did not receive significant attention from the bio-inspired computing community until present time. This study proposes a new variant of Particle Swarm Optimization incorporating behaviour inspired by protozoa and evaluates the performance of such an algorithm on a set of well known test functions. Index Terms—Bio-inspired algorithms; particle swarm opti-mization; protozoic behaviour I.

Cite this paper

APA 7

Behaviour, P., & Abraham, A. (2009). Particle Swarm Optimization. InTech eBooks. https://doi.org/10.5772/109

MLA 9

Behaviour, Protozoic, and Ajith Abraham. “Particle Swarm Optimization.” InTech eBooks, 2009, https://doi.org/10.5772/109.

Chicago

Behaviour, Protozoic, and Ajith Abraham. 2009. “Particle Swarm Optimization.” InTech eBooks. https://doi.org/10.5772/109.

Harvard

Behaviour, P. and Abraham, A. (2009) ‘Particle Swarm Optimization’, InTech eBooks. Available at: https://doi.org/10.5772/109.

BibTeX

@book{behaviour2009particle,
  title = {{Particle Swarm Optimization}},
  author = {Behaviour, Protozoic and Abraham, Ajith},
  publisher = {InTech eBooks},
  year = {2009},
  doi = {10.5772/109}
}

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