Journal of Global Optimization

, Volume 31, Issue 4, pp 579–598 | Cite as

Matching Stochastic Algorithms to Objective Function Landscapes

  • W. P. Baritompa
  • M. Dür
  • E. M. T. Hendrix
  • L. Noakes
  • W. J. Pullan
  • G. R. WoodEmail author


Large scale optimisation problems are frequently solved using stochastic methods. Such methods often generate points randomly in a search region in a neighbourhood of the current point, backtrack to get past barriers and employ a local optimiser. The aim of this paper is to explore how these algorithmic components should be used, given a particular objective function landscape. In a nutshell, we begin to provide rules for efficient travel, if we have some knowledge of the large or small scale geometry.


Backtracking Global optimisation Local optimisation Search region Simulated annealing Temperature 


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  1. 1.
    Baritompa, W., Steel, M. 1996Bounds on absorption times of directionally biased random sequencesRandom Structures and Algorithms9279293Google Scholar
  2. 2.
    Boender C.G.E., Romeijn H.E. (1994). Stochastic Methods In: Horst R., Pardalos P. (ed). Handbook of Global Optimization pp. 829-869. Kluwer Academic Publishers.Google Scholar
  3. 3.
    Hendrix, E.M.T., Ortigosa, P.M., Garcia, I. 2001On success rates for controlled random searchJournal of Global Optimization21239263Google Scholar
  4. 4.
    Pardalos P., Romeijn E. (2002). Handbook of Global Optimization Vol. 2. Kluwer Academic Publishers.Google Scholar
  5. 5.
    Pronzato, L., Walter, E., Venot, A., Lebruchec, J.F. 1984A general purpose global optimizer: Implementation and applicationsMathematics and Computers in Simulation24412422Google Scholar
  6. 6.
    Pullan, W.J. 1996A direct search method applied to a molecular structure problemAustralian Computer Journal28113120Google Scholar
  7. 7.
    Romeijn, H.E., Smith, R.L. 1994Simulated annealing for constrained global optimizationJournal of Global Optimization5101126Google Scholar
  8. 8.
    Schoen, F. 1991Stochastic techniques for global optimization: A survey of recent advancesJournal of Global Optimization1207228Google Scholar
  9. 9.
    Smith, R.L. 1984Efficient Monte Carlo procedures for generating points uniformly distributed over bounded regionsOperations Research3212961308Google Scholar
  10. 10.
    Törn, A., Ali, M.M., Viitanen, S. 1999Stochastic global optimization: Problem classes and solution techniquesJournal of Global Optimization14437447MathSciNetGoogle Scholar
  11. 11.
    Törn A., Zilinskas A. (1989). Global Optimization. Lecture Notes in Computer Science Vol. 350. Springer.Google Scholar
  12. 12.
    Wales, D.J., Doye, J.P.K. 1997Global optimization by basin-hopping and the lowest energy structures of Lennard Jones clusters containing up to 110 atomsJournal of Physical Chemistry A10151115116Google Scholar
  13. 13.
    Wolpert, D.J., Macready, W.G. 1997No free lunch theorems for optimizationIEEE Transactions on Evolutionary Computing16782Google Scholar
  14. 14.
    Zabinsky, Z.B., Smith, R.L., McDonald, J.F., Romeijn, H.E., Kaufman, D.E. 1993Improving Hit and Run for global optimizationJournal of Global Optimization3171192Google Scholar

Copyright information

© Springer 2005

Authors and Affiliations

  • W. P. Baritompa
    • 1
  • M. Dür
    • 2
  • E. M. T. Hendrix
    • 3
  • L. Noakes
    • 4
  • W. J. Pullan
    • 5
  • G. R. Wood
    • 6
    Email author
  1. 1.Department of Mathematics and StatisticsUniversity of CanterburyChristchurchNew Zealand
  2. 2.Department of MathematicsDarmstadt University of TechnologyDarmstadtGermany
  3. 3.Group Operations Research and LogisticsWageningen UniversityWageningenThe Netherlands
  4. 4.School of Mathematics and StatisticsUniversity of Western AustraliaNedlandsAustralia
  5. 5.School of Information TechnologyGriffith UniversityGold CoastAustralia
  6. 6.Department of StatisticsMacquarie UniversityNorth RydeAustralia

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