Soft Computing

, Volume 11, Issue 6, pp 531–540 | Cite as

Multi-Objective Optimization using Grid Computing

  • Antonio J. NebroEmail author
  • Enrique Alba
  • Francisco Luna
Original Paper


This paper analyzes some technical and practical issues concerning the use of parallel systems to solve multi-objective optimization problems using enumerative search. This technique constitutes a conceptually simple search strategy, and it is based on evaluating each possible solution from a given finite search space. The results obtained by enumeration are impractical for most computer platforms and researchers, but they exhibit a great interest because they can be used to be compared against the values obtained by stochastic techniques. We analyze here the use of a grid computing system to cope with the limits of enumerative search. After evaluating the performance of the sequential algorithm, we present, first, a parallel algorithm targeted to multiprocessor systems. Then, we design a distributed version prepared to be executed on a federation of geographically distributed computers known as a computational grid. Our conclusion is that this kind of systems can provide to the community with a large and precise set of Pareto fronts that would be otherwise unknown.


Multi-objective problem optimization Enumerative search Parallel computing Grid computing 


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Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • Antonio J. Nebro
    • 1
    Email author
  • Enrique Alba
    • 2
  • Francisco Luna
    • 3
  1. 1.Departamento de Lenguajes y Ciencias de la Computación, E.T.S. Ingeniería InformáticaUniversidad de MálagaMalagaSpain
  2. 2.Departamento de Lenguajes y Ciencias de la Computación, E.T.S. Ingeniería InformáticaUniversidad de MálagaMalagaSpain
  3. 3.Departamento de Lenguajes y Ciencias de la Computación, E.T.S. Ingeniería InformáticaUniversidad de MálagaMalagaSpain

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