Comparison of Kriging-based algorithms for simulation optimization with heterogeneous noise

DOI10.1016/j.ejor.2017.01.035zbMath1403.90553OpenAlexW2238431993MaRDI QIDQ1753578

Victor Picheny, Hamed Jalali, Inneke Van Nieuwenhuyse

Publication date: 29 May 2018

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2017.01.035

zbMATH Keywords

simulationranking and selectionstochastic Krigingoptimization via simulationheterogeneous noise

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Nonconvex programming, global optimization (90C26) Derivative-free methods and methods using generalized derivatives (90C56) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (15)

Applying Bayesian optimization with Gaussian process regression to computational fluid dynamics problems ⋮ Actor-Critic–Like Stochastic Adaptive Search for Continuous Simulation Optimization ⋮ Surrogate-based feasibility analysis for black-box stochastic simulations with heteroscedastic noise ⋮ A Multilevel Simulation Optimization Approach for Quantile Functions ⋮ Combined Global and Local Search for Optimization with Gaussian Process Models ⋮ Probabilistic bisection with spatial metamodels ⋮ Faster Kriging: Facing High-Dimensional Simulators ⋮ Constrained Bayesian optimization with noisy experiments ⋮ Expected improvement for expensive optimization: a review ⋮ An extended two-stage sequential optimization approach: properties and performance ⋮ A survey on kriging-based infill algorithms for multiobjective simulation optimization ⋮ A multiobjective stochastic simulation optimization algorithm ⋮ Evaluating Gaussian process metamodels and sequential designs for noisy level set estimation ⋮ Analyzing stochastic computer models: a review with opportunities ⋮ Unnamed Item

Uses Software

• EGO
• DiceOptim

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Simulation-optimization approaches for water pump scheduling and pipe replacement problems
• A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival
• Cold supply chain design with environmental considerations: a simulation-optimization approach
• Expected improvement in efficient global optimization through bootstrapped Kriging
• Kriging metamodeling in simulation: a review
• Efficient global optimization of expensive black-box functions
• Simulation budget allocation for further enhancing the efficiency of ordinal optimization
• Noisy kriging-based optimization methods: a unified implementation within the DiceOptim package
• Robustness of kriging when interpolating in random simulation with heterogeneous variances: some experiments
• A taxonomy of global optimization methods based on response surfaces
• Global optimization of stochastic black-box systems via sequential kriging meta-models
• Monte Carlo and quasi-Monte Carlo sampling
• The Knowledge-Gradient Policy for Correlated Normal Beliefs
• Balancing Exploitation and Exploration in Discrete Optimization via Simulation Through a Gaussian Process-Based Search
• Stochastic Kriging for Simulation Metamodeling
• The Correlated Knowledge Gradient for Simulation Optimization of Continuous Parameters using Gaussian Process Regression
• Using Ranking and Selection to “Clean Up” after Simulation Optimization
• The effects of common random numbers on stochastic kriging metamodels
• Stochastic kriging with biased sample estimates
• Design and analysis of simulation experiments
• Finite-time analysis of the multiarmed bandit problem

This page was built for publication: Comparison of Kriging-based algorithms for simulation optimization with heterogeneous noise