Continuous optimization via simulation using Golden Region search |
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Authors: | Alireza Kabirian,Sigurdur Ó lafsson |
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Affiliation: | 1. College of Business and Public Policy, University of Alaska-Anchorage, Rasmuson Hall, 3211 Providence Dr, Anchorage, AK 99508, USA;2. Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Black Engineering Building, Ames, IA 50011, USA |
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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. |
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Keywords: | Continuous optimization Simulation Indifference Zone Probabilistic search Golden Region Metamodeling |
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