The problem of finding a global minimum of a real function on a set S of Rn occurs in many real world problems. Since its computational complexity is exponential, its solution can be a very expensive computational task. In this paper, we introduce a parallel algorithm that exploits the latest computers in the market equipped with more than one processor, and used in clusters of computers. The algorithm belongs to the improvement of local minima algorithm family, and carries on local minimum searches iteratively but trying not to find an already found local optimizer. Numerical experiments have been carried out on two computers equipped with four and six processors; fourteen configurations of the computing resources have been investigated. To evaluate the algorithm performances the speedup and the efficiency are reported for each configuration.

A parallel algorithm for global optimization problems in a distributed computing environment

GAVIANO, MARCO;LERA, DANIELA;
2012-01-01

Abstract

The problem of finding a global minimum of a real function on a set S of Rn occurs in many real world problems. Since its computational complexity is exponential, its solution can be a very expensive computational task. In this paper, we introduce a parallel algorithm that exploits the latest computers in the market equipped with more than one processor, and used in clusters of computers. The algorithm belongs to the improvement of local minima algorithm family, and carries on local minimum searches iteratively but trying not to find an already found local optimizer. Numerical experiments have been carried out on two computers equipped with four and six processors; fourteen configurations of the computing resources have been investigated. To evaluate the algorithm performances the speedup and the efficiency are reported for each configuration.
2012
Random search; Global optimization; Parallel computing
File in questo prodotto:
File Dimensione Formato  
articoloAM.pdf

accesso aperto

Tipologia: versione editoriale
Dimensione 235.29 kB
Formato Adobe PDF
235.29 kB Adobe PDF Visualizza/Apri

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/11584/101192
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact