Particle swarm based collective searching model for adaptive environment

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations

Abstract

This report presents a pilot study of an integration of particle swarm algorithm, social knowledge adaptation and multi-agent approaches for modeling the collective search behavior of self-organized groups in an adaptive environment. The objective of this research is to apply the particle swarm metaphor as a model of social group adaptation for the dynamic environment and to provide insight and understanding of social group knowledge discovering and strategic searching. A new adaptive environment model, which dynamically reacts to the group collective searching behaviors, is proposed in this research. The simulations in the research indicate that effective communication between groups is not the necessary requirement for whole self-organized groups to achieve the efficient collective searching behavior in the adaptive environment. One possible application of this research is building scientific understanding of the insurgency in the count-Insurgent warfare. 2008 Springer-Verlag Berlin Heidelberg.

Original languageEnglish
Title of host publicationNature Inspired Cooperative Strategies for Optimization (NICSO 2007)
EditorsNatalio Krasnogor, Giuseppe Nicosia, Mario Pavone, David Pelta
Pages211-220
Number of pages10
DOIs
StatePublished - 2008

Publication series

NameStudies in Computational Intelligence
Volume129
ISSN (Print)1860-949X

Fingerprint

Dive into the research topics of 'Particle swarm based collective searching model for adaptive environment'. Together they form a unique fingerprint.

Cite this