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Continuous optimization via simulation using Golden Region search

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  • Kabirian, Alireza
  • Ólafsson, Sigurdur

Abstract

Simulation Optimization (SO) is a class of mathematical optimization techniques in which the objective function can only be numerically evaluated through simulation. In this paper, a new SO approach called Golden Region (GR) search is developed for continuous problems. GR divides the feasible region into a number of (sub) regions and selects one region in each iteration for further search based on the quality and distribution of simulated points in the feasible region and the result of scanning the response surface through a metamodel. Monte Carlo experiments show that the GR method is efficient compared to three well-established approaches in the literature. We also prove the asymptotic convergence in probability to a global optimum for a large class of random search methods in general and GR in particular.

Suggested Citation

  • Kabirian, Alireza & Ólafsson, Sigurdur, 2011. "Continuous optimization via simulation using Golden Region search," European Journal of Operational Research, Elsevier, vol. 208(1), pages 19-27, January.
    • Handle: RePEc:eee:ejores:v:208:y:2011:i:1:p:19-27
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    References listed on IDEAS

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    1. Sujit De & Shib Sana, 2015. "Backlogging EOQ model for promotional effort and selling price sensitive demand- an intuitionistic fuzzy approach," Annals of Operations Research, Springer, vol. 233(1), pages 57-76, October.

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