2018
DOI: 10.1109/tsmc.2017.2679192
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Efficient Ranking and Selection for Stochastic Simulation Model Based on Hypothesis Test

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Cited by 17 publications
(37 citation statements)
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“…The parameters of GA in Algorithm 2 were set as follows according to the typical Dejong setting [64]: k = 50, G max = 1000, e = 15, P c = 0 5, and P m = 0 01. The parameters of R&S were set as follows according to the efficient setting guideline [43]: T = 5000, n 0 = 10, Δ = 50, and m = 30. As a result, we could find the optimal design that maximizes the LER, [20,20,20,OC4,20,20,type 4], among 16,384 alternatives very efficiently via applying Algorithm 2.…”
Section: Experimental Design and Resultsmentioning
confidence: 99%
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“…The parameters of GA in Algorithm 2 were set as follows according to the typical Dejong setting [64]: k = 50, G max = 1000, e = 15, P c = 0 5, and P m = 0 01. The parameters of R&S were set as follows according to the efficient setting guideline [43]: T = 5000, n 0 = 10, Δ = 50, and m = 30. As a result, we could find the optimal design that maximizes the LER, [20,20,20,OC4,20,20,type 4], among 16,384 alternatives very efficiently via applying Algorithm 2.…”
Section: Experimental Design and Resultsmentioning
confidence: 99%
“…On the other hand, if k is finite and relatively small (specifically, all alternatives can be simulated more than five times), the ranking and selection (R&S) methods in statistics, such as OCBA [41], KN [42], and UE [43], can be considered 3 Complexity efficient approaches. Unlike classical metaheuristic methods, statistical R&S performs simulations for all alternatives.…”
Section: Optimization In the Stochastic Simulation Modelmentioning
confidence: 99%
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“…Для имитационных моделей, содержащих сравнительно небольшое число параметров (от нескольких десятков до нескольких сотен), показали удовлетворительные результаты известные оптимизационные алгоритмы имитации отжига (simulated annealing), генетические (genetic algorithm), поиска с запретами (tabu search). При наличии в модели нескольких тысяч параметров большое распространение получили методы статистического ранжирования и выбора (ranking and selection) [Chen et al, 2014;Choi, Kim, 2018;Fu et al, 2008], а также различные регрессионные алгоритмы [Ankenman et al, 2010;Kleijnen, 2009]:…”
Section: рис 3 общая схема поиска оптимального решения при использоunclassified
“…In addition, it may lower the performance of PSO by reducing the number of iterations of PSO under a limited computing budget. Thus, the resampling approach combined ranking and selection (R&S) methods, such as the indifference-zone (IZ) [9], the optimal computing budget allocation (OCBA) [10], and the uncertainty evaluation (UE) [11], into PSO for efficient sample allocations. These R&S methods intelligently allocate a limited number of samples to correctly select the best solution from a finite set of alternatives.…”
Section: Introductionmentioning
confidence: 99%