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The sample-path method is one of the most important tools in simulation-based optimization. The basic idea of the method is to approximate the expected simulation output by the average of sample observations with a common random number sequence. In this paper, we describe a new variant of Powell’s UOBYQA (Unconstrained Optimization BY Quadratic(More)
Computer simulations are used extensively as models of real systems to evaluate output responses. The choice of optimal simulation parameters can lead to improved operation, but configuring them well remains a challenging problem. Simulation-based optimization is an emerging field which integrates optimization techniques into simulation analysis. The(More)
We describe the application of a Bayesian variable-number sample-path (VNSP) optimization algorithm to yield a robust design for a floating sleeve antenna for hepatic microwave ablation. Finite element models are used to generate the electromagnetic (EM) field and thermal distribution in liver given a particular design. Dielectric properties of the tissue(More)
DIRECT (DIviding RECTangles) is a deterministic global optimization algorithm for bound-constrained problems. The algorithm, based on a space-partitioning scheme, performs both global exploration and local exploitation. In this paper, we modify the deterministic DIRECT algorithm to handle noisy function optimization. We adopt a simple approach that(More)
We propose a robust portfolio optimization approach based on Value-at-Risk (VaR) adjusted Sharpe ratios. Traditional Sharpe ratio estimates using a limited series of historical returns are subject to estimation errors. Portfolio optimization based on traditional Sharpe ratios ignores this uncertainty and, as a result, is not robust. In this paper, we(More)
In many real-world optimization problems, the objective function may come from a simulation evaluation so that it is (a) subject to various levels of noise, (b) not differentiable, and (c) computationally hard to evaluate. In this paper, we modify Powell's UOBYQA algorithm to handle those real-world simulation problems. Our modifications apply Bayesian(More)
We investigate the use of optimization and data mining techniques for calibrating the input parameters to a discrete event simulation code. In the context of a breast-cancer epidemiology model we show how a hierarchical classifier can accurately predict those parameters that ensure the simulation replicates benchmark data within 95% confidence intervals. We(More)
In simulation-based optimization, we seek the optimal parameter settings that minimize or maximize certain performance measures of the simulation system. In this paper, we use a two-phase approach to calibrate simulation parameters using classification tools. This classification-based method is used in Phase I to facilitate the global search process and it(More)
The halogen bond, similar to the hydrogen bond, is an important noncovalent interaction and plays important roles in diverse chemistry-related fields. Herein, bromine- and iodine-based halogen-bonding interactions between two benzene derivatives (C6 F5 Br and C6 F5 I) and dimethyl sulfoxide (DMSO) are investigated by using IR and NMR spectroscopy and ab(More)