Stochastic optimization using grey wolf optimization with optimal computing budget allocation
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[1] Loo Hay Lee,et al. Optimal computing budget allocation for complete ranking with input uncertainty , 2020 .
[2] Cheng Zhang,et al. Modeling and simulating the impact of forgetting and communication errors on delays in civil infrastructure shutdowns , 2020 .
[3] Leyuan Shi,et al. A New Budget Allocation Framework for the Expected Opportunity Cost , 2017, Oper. Res..
[4] Bijaya K. Panigrahi,et al. Binary Grey Wolf Optimizer for large scale unit commitment problem , 2018, Swarm Evol. Comput..
[5] MengChu Zhou,et al. Scheduling Dual-Objective Stochastic Hybrid Flow Shop With Deteriorating Jobs via Bi-Population Evolutionary Algorithm , 2020, IEEE Transactions on Systems, Man, and Cybernetics: Systems.
[6] Ming Liu,et al. A green-oriented bi-objective disassembly line balancing problem with stochastic task processing times , 2020, Ann. Oper. Res..
[7] Parham Pahlavani,et al. An efficient modified grey wolf optimizer with Lévy flight for optimization tasks , 2017, Appl. Soft Comput..
[8] Zhi-jun Teng,et al. An improved hybrid grey wolf optimization algorithm , 2018, Soft Computing.
[9] Barry L. Nelson,et al. A fully sequential procedure for indifference-zone selection in simulation , 2001, TOMC.
[10] Tianyou Chai,et al. Generalized Multitasking for Evolutionary Optimization of Expensive Problems , 2019, IEEE Transactions on Evolutionary Computation.
[11] Fei Gao,et al. A new strategy for selecting good enough designs using optimal computing budget allocation , 2018, J. Simulation.
[12] Shixin Liu,et al. Lexicographic Multiobjective Scatter Search for the Optimization of Sequence-Dependent Selective Disassembly Subject to Multiresource Constraints , 2020, IEEE Transactions on Cybernetics.
[13] Loo Hay Lee,et al. Optimal Computing Budget Allocation to Select the Nondominated Systems—A Large Deviations Perspective , 2018, IEEE Transactions on Automatic Control.
[14] Loo Hay Lee,et al. Ranking and selection for terminating simulation under sequential sampling , 2020, IISE Trans..
[15] Chun-Hung Chen,et al. Simulation Budget Allocation for Further Enhancing the Efficiency of Ordinal Optimization , 2000, Discret. Event Dyn. Syst..
[16] Stephen E. Chick,et al. New Two-Stage and Sequential Procedures for Selecting the Best Simulated System , 2001, Oper. Res..
[17] MengChu Zhou,et al. Stochastic multi-objective integrated disassembly-reprocessing-reassembly scheduling via fruit fly optimization algorithm , 2021 .
[18] MengChu Zhou,et al. Approximate Simulation Budget Allocation for Subset Ranking , 2017, IEEE Transactions on Control Systems Technology.
[19] Ram Sarkar,et al. Selective Opposition based Grey Wolf Optimization , 2020, Expert Syst. Appl..
[20] Yan Wang,et al. Facility location and capacity planning considering policy preference and uncertain demand under the One Belt One Road initiative , 2020 .
[21] Makarand M. Lokhande,et al. Grey wolf algorithm for multidimensional engine optimization of converted plug-in hybrid electric vehicle , 2018 .
[22] MengChu Zhou,et al. Multiverse Optimization Algorithm for Stochastic Biobjective Disassembly Sequence Planning Subject to Operation Failures , 2021, IEEE Transactions on Systems, Man, and Cybernetics: Systems.
[23] Chao Zhang,et al. Application of Grey Wolf Optimization for Solving Combinatorial Problems: Job Shop and Flexible Job Shop Scheduling Cases , 2018, IEEE Access.
[24] Loo Hay Lee,et al. Efficient Simulation Budget Allocation for Selecting an Optimal Subset , 2008, INFORMS J. Comput..
[25] Jun Lu,et al. Construction of prediction intervals for carbon residual of crude oil based on deep stochastic configuration networks , 2019, Inf. Sci..
[26] Chaoyong Zhang,et al. Stochastic multi-objective modelling and optimization of an energy-conscious distributed permutation flow shop scheduling problem with the total tardiness constraint , 2019, Journal of Cleaner Production.
[27] Jinliang Ding,et al. Two-objective stochastic flow-shop scheduling with deteriorating and learning effect in Industry 4.0-based manufacturing system , 2017, Appl. Soft Comput..
[28] Yu-Chi Ho,et al. An ordinal optimization approach to optimal control problems , 1999, Autom..
[29] Jie Xu,et al. A Study on Efficient Computing Budget Allocation for a Two-Stage Problem , 2020 .
[30] Loo Hay Lee,et al. Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Optimization , 2017, IEEE Transactions on Evolutionary Computation.
[31] Songfeng Lu,et al. Chaotic opposition-based grey-wolf optimization algorithm based on differential evolution and disruption operator for global optimization , 2018, Expert Syst. Appl..
[32] Hui Xiao,et al. An efficient simulation procedure for ranking the top simulated designs in the presence of stochastic constraints , 2019, Autom..
[33] Yu-Chi Ho,et al. Ordinal optimization of DEDS , 1992, Discret. Event Dyn. Syst..
[34] Hui Xiao,et al. Simulation optimization using genetic algorithms with optimal computing budget allocation , 2014, Simul..
[35] Hossam Faris,et al. Grey wolf optimizer: a review of recent variants and applications , 2017, Neural Computing and Applications.
[36] Andrew Lewis,et al. Grey Wolf Optimizer , 2014, Adv. Eng. Softw..
[37] Liang Gao,et al. A multi-objective discrete flower pollination algorithm for stochastic two-sided partial disassembly line balancing problem , 2019, Comput. Ind. Eng..
[38] Loo Hay Lee,et al. A Simulation Budget Allocation Procedure for Enhancing the Efficiency of Optimal Subset Selection , 2016, IEEE Transactions on Automatic Control.
[39] Jianhua Gu,et al. Evolving an optimal kernel extreme learning machine by using an enhanced grey wolf optimization strategy , 2019, Expert Syst. Appl..
[40] Yaoming Zhou,et al. On connectivity of post-earthquake road networks , 2019 .
[41] Junwei Wang,et al. Multiobjective Modeling and Optimization for Scheduling a Stochastic Hybrid Flow Shop With Maximizing Processing Quality and Minimizing Total Tardiness , 2021, IEEE Systems Journal.
[42] Hui Xiao,et al. Simulation Budget Allocation for Selecting the Top-m Designs With Input Uncertainty , 2018, IEEE Transactions on Automatic Control.
[43] Y. Rinott. On two-stage selection procedures and related probability-inequalities , 1978 .
[44] Nan Liu,et al. The defect of the Grey Wolf optimization algorithm and its verification method , 2019, Knowl. Based Syst..
[45] Hui Xiao,et al. Robust ranking and selection with optimal computing budget allocation , 2017, Autom..
[46] Jing Chen,et al. Optimal decisions on prices, order quantities, and returns policies in a supply chain with two-period selling , 2021, Eur. J. Oper. Res..
[47] Jürgen Branke,et al. Selecting a Selection Procedure , 2007, Manag. Sci..
[48] Zhiwu Li,et al. Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm , 2018, Journal of Intelligent Manufacturing.
[49] Fehmi Burcin Ozsoydan,et al. Effects of dominant wolves in grey wolf optimization algorithm , 2019, Appl. Soft Comput..
[50] Loo Hay Lee,et al. Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data , 2015, Asia Pac. J. Oper. Res..