Adaptive random search for continuous simulation optimization

Many systems arising in applications from engineering design, manufacturing, and healthcare require the use of simulation optimization (SO) techniques to improve their performance. In “Actor-Critic–Like Stochastic Adaptive Search for Continuous Simulation Optimization,” Q. Zhang and J. Hu propose a randomized approach that integrates ideas from actor-critic reinforcement learning within a class of adaptive search algorithms for solving SO problems. The approach fully retains the previous simulation data and incorporates them into an approximation architecture to exploit knowledge of the objective function in searching for improved solutions. The authors provide a finite-time analysis for the method when only a single simulation observation is collected at each iteration. The method works well on a diverse set of benchmark problems and has the potential to yield good performance for complex problems using expensive simulation experiments for performance evaluation.

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Surrogate-Based Promising Area Search for Lipschitz Continuous Simulation Optimization

INFORMS Journal on Computing ◽

10.1287/ijoc.2017.0801 ◽

2018 ◽

Vol 30 (4) ◽

pp. 677-693

Author(s):

Qi Fan ◽

Jiaqiao Hu

Keyword(s):

Simulation Optimization ◽

Continuous Simulation ◽

Lipschitz Continuous ◽

Promising Area

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Constructing Synthetic Samples

Journal of Official Statistics ◽

10.1515/jos-2016-0005 ◽

2016 ◽

Vol 32 (1) ◽

pp. 113-127

Author(s):

Hua Dong ◽

Glen Meeden

Keyword(s):

Optimization Problem ◽

Search Algorithm ◽

Random Search ◽

The United States ◽

Simulation Studies ◽

Synthetic Sample ◽

Population Means ◽

Adaptive Random Search ◽

Random Search Algorithm ◽

Two Populations

Abstract We consider the problem of constructing a synthetic sample from a population of interest which cannot be sampled from but for which the population means of some of its variables are known. In addition, we assume that we have in hand samples from two similar populations. Using the known population means, we will select subsamples from the samples of the other two populations which we will then combine to construct the synthetic sample. The synthetic sample is obtained by solving an optimization problem, where the known population means, are used as constraints. The optimization is achieved through an adaptive random search algorithm. Simulation studies are presented to demonstrate the effectiveness of our approach. We observe that on average, such synthetic samples behave very much like actual samples from the population of interest. As an application we consider constructing a one-percent synthetic sample for the missing 1890 decennial sample of the United States.

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A Bio-Inspired Robust Adaptive Random Search Algorithm for Distributed Beamforming

2011 IEEE International Conference on Communications (ICC) ◽

10.1109/icc.2011.5962498 ◽

2011 ◽

Cited By ~ 7

Author(s):

Chia-Shiang Tseng ◽

Chang-Ching Chen ◽

Che Lin

Keyword(s):

Search Algorithm ◽

Random Search ◽

Distributed Beamforming ◽

Adaptive Random Search ◽

Random Search Algorithm ◽

Robust Adaptive

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Numerical Optimization of the Thermoelectric Cooling Devices

Journal of Electronic Packaging ◽

10.1115/1.2753959 ◽

2006 ◽

Vol 129 (3) ◽

pp. 339-347 ◽

Cited By ~ 28

Author(s):

Boris Abramzon

Keyword(s):

Electric Current ◽

Optimization Problems ◽

Random Search ◽

Thermal Conductance ◽

Numerical Approach ◽

Physical Parameters ◽

Optimization Approach ◽

Independent Variables ◽

Adaptive Random Search ◽

Optimization Search

The present study proposes a unified numerical approach to the problem of optimum design of the thermoelectric devices for cooling electronic components. The standard mathematical model of a single-stage thermoelectric cooler (TEC) with constant material properties is employed. The model takes into account the thermal resistances from the hot and cold sides of the TEC. Values of the main physical parameters governing the TEC performance (Seebeck coefficient, electrical resistance, and thermal conductance) are derived from the manufacturer catalog data on the maximum achievable temperature difference, and the corresponding electric current and voltage. The optimization approach is illustrated with several examples for different design objective functions, variables, and constraints. The objective for the optimization search is the maximization of the total cooling rate or the performance coefficient of the cooling device. The independent variables for the optimization search are as follows: The number of the thermoelectric modules, the electric current, and the cold side temperature of the TEC. Additional independent variables in other cases include the number of thermoelectric couples and the area-to-height ratio of the thermoelectric pellet. In the present study, the optimization problems are solved numerically using the so-called multistart adaptive random search method.

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Simulation Optimization Using Multi-Time-Scale Adaptive Random Search

Asia Pacific Journal of Operational Research ◽

10.1142/s0217595919400141 ◽

2019 ◽

Vol 36 (06) ◽

pp. 1940014

Author(s):

Qi Zhang ◽

Jiaqiao Hu

Keyword(s):

Search Algorithm ◽

Numerical Study ◽

Random Search ◽

Solution Space ◽

Population Based ◽

Global Optimum ◽

Adaptive Random Search ◽

Candidate Solution ◽

Approximation Type ◽

Effective Use

We propose a random search algorithm for seeking the global optimum of an objective function in a simulation setting. The algorithm can be viewed as an extension of the MARS algorithm proposed in Hu and Hu (2011) for deterministic optimization, which iteratively finds improved solutions by modifying and sampling from a parameterized probability distribution over the solution space. However, unlike MARS and many other algorithms in this class, which are often population-based, our method only requires a single candidate solution to be generated at each iteration. This is primarily achieved through an effective use of past sampling information by means of embedding multiple nested stochastic approximation type of recursions into the algorithm. We prove the global convergence of the algorithm under general conditions and discuss two special simulation noise cases of interest, in which we show that only one simulation replication run is needed for each sampled solution. A preliminary numerical study is also carried out to illustrate the algorithm.

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