In this paper, we describe the efficient implementation of M-Sparrow, an adaptive flocking algorithm based on the biology-inspired paradigm of a flock of birds. We extended the classical flock model of Reynolds with two new characteristics: the movement in a multi-dimensional space and different kinds of birds. The birds, in this context, are used to discovery point having some desired characteristics in a multidimensional space. A critical point of the algorithm is the efficient search of the k-neighbors in a multidimensional space. This search was efficiently implemented using the ANN libraries.

A multidimensional flocking algorithm for clustering spatial data

Augimeri A;Folino G;Forestiero A;Spezzano;
2006

Abstract

In this paper, we describe the efficient implementation of M-Sparrow, an adaptive flocking algorithm based on the biology-inspired paradigm of a flock of birds. We extended the classical flock model of Reynolds with two new characteristics: the movement in a multi-dimensional space and different kinds of birds. The birds, in this context, are used to discovery point having some desired characteristics in a multidimensional space. A critical point of the algorithm is the efficient search of the k-neighbors in a multidimensional space. This search was efficiently implemented using the ANN libraries.
2006
Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR
Clustering spatial data
Critical points
Efficient implementation
Flock of Birds
Flocking algorithms
Multi-dimensional space
Reynolds
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/192097
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact