Efficient Ranking and Selection for Stochastic Simulation Model Based on Hypothesis Test

Kim, Tag Gon

doi:10.1109/tsmc.2017.2679192

Cited by 17 publications

(37 citation statements)

References 36 publications

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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%

“…Among various R&S algorithms, we apply the UEmr algorithm which is the most recently developed based on the uncertainty evaluation (UE) framework [43]. Algorithm 1 represents the UEmr algorithm (see [50] for detail).…”

Section: Optimization Processmentioning

confidence: 99%

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Simulation‐Based Optimization on the System‐of‐Systems Model via Model Transformation and Genetic Algorithm: A Case Study of Network‐Centric Warfare

et al. 2018

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Simulation of a system-of-systems (SoS) model, which consists of a combat model and a network model, has been used to analyze the performance of network-centric warfare in detail. However, finding the combat model parameters satisfying the required combat power using simulation can take a long time for two reasons: (1) the prolonged execution time per simulation run and (2) the enormous number of simulation runs. This paper proposes a simulation-based optimization method for the SoS-based simulation model to overcome these problems. The method consists of two processes: (1) the transformation of the SoS-based model into an integrated model using the neural network to reduce the execution time and (2) the optimization of the integrated model using the genetic algorithm with ranking and selection to decrease the number of simulation runs. The experimental result reveals that the proposed method significantly reduced the time for finding the optimal combat parameters with an acceptable level of accuracy.

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Section: Experimental Design and Resultsmentioning

confidence: 99%

Section: Optimization In the Stochastic Simulation Modelmentioning

confidence: 99%

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Simulation‐Based Optimization on the System‐of‐Systems Model via Model Transformation and Genetic Algorithm: A Case Study of Network‐Centric Warfare

et al. 2018

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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

The purposeful transformation of mathematical models based on strategic reflection

Antipova¹,

Vorobiev²

2019

CRM

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The study of complex processes in various spheres of human activity is traditionally based on the use of mathematical models. In modern conditions, the development and application of such models is greatly simplified by the presence of high-speed computer equipment and specialized tools that allow, in fact, designing models from pre-prepared modules. Despite this, the known problems associated with ensuring the adequacy of the model, the reliability of the original data, the implementation in practice of the simulation results, the excessively large dimension of the original data, the joint application of sufficiency heterogeneous mathematical models in terms of complexity and integration of the simulated processes are becoming increasingly important. The more critical may be the external constraints imposed on the value of the optimized functional, and often unattainable within the framework of the constructed model. It is logical to assume that in order to fulfill these restrictions, a purposeful transformation of the original model is necessary, that is, the transition to a mathematical model with a deliberately improved solution. The new model will obviously have a different internal structure (a set of parameters and their interrelations), as well as other formats (areas of definition) of the source data. The possibilities of purposeful change of the initial model investigated by the authors are based on the realization of the idea of strategic reflection. The most difficult in mathematical terms practical implementation of the author's idea is the use of simulation models, for which the algorithms for finding optimal solutions have known limitations, and the study of sensitivity in most cases is very difficult. On the example of consideration of rather standard discrete-event simulation model the article presents typical methodological techniques that allow ranking variable parameters by sensitivity and, in the future, to expand the scope of definition of variable parameter to which the simulation model is most sensitive. In the transition to the "improved" model, it is also possible to simultaneously exclude parameters from it, the influence of which on the optimized functional is insignificant, and vice versa-the introduction of new parameters corresponding to real processes into the model.

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“…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%

An Effective Adjustment to the Integration of Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Environments

Bae

2020

IEEE Access

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Although particle swarm optimization (PSO) is a powerful evolutionary algorithm for solving nonlinear optimization problems in deterministic environments, many practical problems have some stochastic noise. The optimal computing budget allocation (OCBA) has been integrated into PSO in various ways to cope with this. The OCBA can mitigate the effect of noise on PSO by selecting the best solution under a limited evaluation budget. Recently, with the increasing complexity of PSO applications, the evaluation costs are also increasing rapidly, which has sparked the need for more efficient PSO in stochastic environments. This paper proposes a simple yet effective adjustment to the integration of OCBA to further improve the efficiency of PSO. The proposed adjustment allows OCBA to expand its search space to find the global best position more correctly such that the entire swarm can move on a better direction under stochastic noise. The experimental results on various benchmarks demonstrate the improved performance of PSO by the proposed adjustment under a limited budget compared with the latest studies. In addition, the results regarding fighters' evasion flight optimization emphasize the practical need for the proposed adjustment.

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Efficient Ranking and Selection for Stochastic Simulation Model Based on Hypothesis Test

Cited by 17 publications

References 36 publications

Simulation‐Based Optimization on the System‐of‐Systems Model via Model Transformation and Genetic Algorithm: A Case Study of Network‐Centric Warfare

Simulation‐Based Optimization on the System‐of‐Systems Model via Model Transformation and Genetic Algorithm: A Case Study of Network‐Centric Warfare

The purposeful transformation of mathematical models based on strategic reflection

An Effective Adjustment to the Integration of Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Environments

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