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Journal of Global Optimization

, Volume 33, Issue 2, pp 235–255 | Cite as

Experimental Testing of Advanced Scatter Search Designs for Global Optimization of Multimodal Functions

  • Manuel Laguna
  • Rafael Martí
Article

Abstract

Scatter search is an evolutionary method that, unlike genetic algorithms, operates on a small set of solutions and makes only limited use of randomization as a proxy for diversification when searching for a globally optimal solution. The scatter search framework is flexible, allowing the development of alternative implementations with varying degrees of sophistication. In this paper, we test the merit of several scatter search designs in the context of global optimization of multimodal functions. We compare these designs among themselves and choose one to compare against a well-known genetic algorithm that has been specifically developed for this class of problems. The testing is performed on a set of benchmark multimodal functions with known global minima.

Keywords

scatter search metaheuristic optimization nonlinear optimization 

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References

  1. 1.
    Campos, V., Glover, F., Laguna, M., Martí, R. 2001An experimental evaluation of a scatter search for the linear ordering problemJournal of Global Optimization21397414CrossRefGoogle Scholar
  2. 2.
    Glover, F. 1994Tabu search for nonlinear and parametric optimization (with Links to Genetic Algorithms)Discrete Applied Mathematics49231255CrossRefGoogle Scholar
  3. 3.
    Glover, F. 1998A template for scatter search and path relinkingHao, J.-K.Lutton, E.Ronald, E.Schoenauer, M.Snyers, D. eds. Artificial Evolution, Lecture Notes in Computer Science, 1363Springer-VerlagNew York1354Google Scholar
  4. 4.
    Laguna, M. 2002Scatter searchPardalos, P.M.Resende, M.G.C. eds. Handbook of Applied OptimizationOxford University PressNew York183193Google Scholar
  5. 5.
    Laguna, M., Martí, R. 2003Scatter Search. Methodology and ImplementationsKluwer Academic PublishersDordrechtGoogle Scholar
  6. 6.
    Martí, R., Laguna, M. and Glover, F. (2004), Principles of Scatter Search, European Journal of Operational Research, forthcoming.Google Scholar
  7. 7.
    Michalewicz, Z. 1994Genetic Algorithms + Data Structures = Evolution Programs, Second-Extended EditionSpringer-VerlagNew YorkGoogle Scholar
  8. 8.
    Michalewicz, Z., Logan, T.D. 1994Evolutionary Operators for Continuous Convex Parameter SpacesSebald, A.V.Fogel, L.J. eds. Proceedings of the Third Annual Conference on Evolutionary ProgrammingWorld Scientific PublishingRiver Edge, NJ8497Google Scholar
  9. 9.
    Roy, R. K. 1990A Primer on the Taguchi MethodVan Nostrand ReinholdNew YorkGoogle Scholar

Copyright information

© Springer 2005

Authors and Affiliations

  1. 1.Leeds School of BusinessUniversity of ColoradoBoulderUSA
  2. 2.Departamento de Estadística e Investigación OperativaUniversitat de ValènciaBurjassot (Valencia)Spain

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