A stochastic approximation approach to spatio-temporal anchorage planning with multiple objectives

This paper describes the implementation of a self- optimizing embedded control scheme for an induction motor drive. The online design problem is formulated as a search problem and solved with a stochastic optimization algorithm. The objective function takes in account the tracking error, and is directly measured on the hardware bench. The online optimization is performed with the simultaneous perturbation stochastic approximation (SPSA) algorithms, which offer a very effective tradeoff between simplicity of implementation, speed of convergence and quality of the final solutions. Among the known SPSA algorithms considered in this paper, we also propose a novel variant inspired to the concept of elitism frequently used in evolutionary computation. To assess the relative performances of the various algorithms, the paper carries out a comprehensive analysis of a control scheme for an induction motor drive subject to time-varying load disturbances.

The purpose of this study is to determine the optimum design of arch longitudinal no prismatic single-cell section. The FE model of the Cetina open spandrel arch bridge was constructed using the ANSYS. In this present work the optimum design is carried out by taking total material volume of substructure of bridge as objective function. Substructure includes of column and reinforced concrete arch. Finally, the optimization technique is performed by Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm. It is concluded that SPSA can be effectively used in the shape optimization of the bridges. Keywords: Long Span, Longitudinal Section Optimization, Open Spandrel Bridge, SPSA Algorithm, Substructure

Adaptive complementary filter using fuzzy logic and simultaneous perturbation stochastic approximation algorithm

Rail simulation model calibration is a process of adjusting model parameters while comparing model output with observations from the real rail system. There is a lack of systematic methodology for calibrating urban rail simulation models. Based on a simulator developed for urban rail operations and control, the paper demonstrates a methodology of calibrating model parameters, and specifically, fine-tuning some of the simulation inputs. The calibration process is modeled as a multi-variate optimization problem and solved by the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm. A case study of the Massachusetts Bay Transportation Authority (MBTA) Red Line shows that the methodology improves the simulation model dramatically in terms of replicating the track block runtimes. At the same time, it upgrades the station specific dwell time parameters and enhances a-priori boarding rates at stations fairly effectively.

In recent years, simulation optimization has attracted a great deal of attention because simulation can model the real systems in fidelity and capture complex dynamics. Among numerous simulation optimization algorithms, Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm is an attractive approach because of its simplicity and efficiency. Although SPSA has been applied in several problems, it does not converge for some. This research proposes Augmented Simultaneous Perturbation Stochastic Approximation (ASPSA) algorithm in which SPSA is augmented to include presearch, ordinal optimization, non-uniform gain, and line search. Performances of ASPSA are tested on complex discrete supply chain inventory optimization problems. The tests results show that ASPSA not only achieves speed up, but also improves solution quality and converges faster than SPSA. Experiments also show that ASPSA is comparable to Genetic Algorithms in solution quality (6% to 15% worse) but is much more efficient computationally (over 12x faster).Request access from your librarian to read this chapter's full text.

This study proposes a data-driven PID tuning for liquid slosh suppression based on enhanced stochastic approximation. In particular, a new version of Simultaneous Perturbation Stochastic Approximation (SPSA) based on memory type function is introduced. This memory-based SPSA (M-SPSA) algorithm has the capability to obtain a better optimization accuracy than the conventional SPSA since it is able to keep the best design parameter during the tuning process. The effectiveness of this algorithm is tested to data-drive PID tuning for liquid slosh problem. The achievement of the M-SPSA based algorithm is assessed in terms of trajectory tracking of trolley position, slosh angle reduction and also computation time. The outcome of this study shows that the PID-tuned M-SPSA is able to provide better control performance accuracy than the other variant of SPSA based method.

The simultaneous perturbation stochastic approximation(SPSA) is an iterative gradient-free optimization algorithm. This algorithm approximates the optimal solution by estimating the gradient of the loss function and uses only 2 values of the loss function to estimate the gradient which is independent of the dimension of the problem. But for the high dimensional problem with large-scale, the convergence of classical SPSA algorithm is slow, even can not converge to the global minimum value. In order to accelerate the convergence of SPSA algorithm and improve the precision of the optimum solution, this paper proposes an improvement of SPSA algorithm based on the adaptive step sizes, which can adjust the step size of each iteration according to the gradient direction. The modified algorithm is implemented by Message Passing Interface (MPI) parallel in each iteration to widen the search scope for the better descending direction in order to accelerate the convergence. Numerical experiments of several classical nonlinear optimization problems shows that the modified SPSA algorithm can accelerate the convergence of nonlinear optimization problems and find the global extremum point of high-dimension for some optimization problems which the SPSA algorithm can not find. It proves that the modified algorithm with adaptive step sizes outperforms the classical SPSA algorithm.

Limited by the computing capacity of current computer and difficulty in inversion of large quantity of reservoir parameters for large scale reservoir history matching, the current automatic history matching methods are still hard to be applied in real fields. A parameterization method based on matrix decomposition is proposed to reduce the computational cost and improve the efficiency of history matching without decreasing the precision by mapping the original optimization problem from high dimensional space to low dimensional space. Base on the updated reservoir model after history matching, a reservoir production optimization control model is established. Considering the complexity of gradient calculation for history matching and production optimization objective function, a new general upscaling stochastic approximation (GUSA) algorithm is given based on the approximation perturbation gradient algorithm. The GUSA algorithm aims to get maximum increment of an objective function in unit step by obtaining an optimal lower triangular matrix in each iterative step. Toy problem results show that the calculation efficiency is improved much compared with the simultaneous perturbation stochastic approximation (SPSA) algorithm. For application of a real reservoir, a good history matching result is obtained for historic production data that verifies the validity of parameterization method and GUSA algorithm. An optimal control for production optimization performs an increase of oil production and a decrease of water cut. Besides, the optimal production scheme is accordant with the practical characteristics of dynamic production, which also proves the availability of GUSA algorithm.KeywordsHistory matchingParameterization methodProduction optimal controlApproximate gradientOptimization algorithm

Summary The main objective of the present work is to numerically determine how sensor information may aid in reducing the ill-posedness associated with permeability estimation via 4D seismic history matching. These sensors are assumed to provide timely information of pressures, concentrations and fluid velocities at given locations in a reliable fashion. This information is incorporated into an objective function that additionally includes production and seismic components that are mismatched between observed and predicted data. In order to efficiently perform large-scale permeability estimation, a coupled multilevel, stochastic and learning search methodology is proposed. At a given resolution level, the parameter space is globally explored and sampled by the simultaneous perturbation stochastic approximation (SPSA) algorithm. The estimation and sampling performed by SPSA is further enhanced by a neural learning engine that estimates sensitivities in the vicinity of the most promising optimal solutions. Preliminary results shed light on future research avenues for optimizing the frequency and localization of 4-D seismic surveys when sensor data is

Considering the problem of the highly dependence on the accuracy model information of the industrial control process, an online PID parameter tuning method is proposed based on the simultaneous perturbation stochastic approximation (SPSA) algorithm and the traditional PID control only by the input and output data of the system. Interfering with each component of the PID controller parameters simultaneously, and the gradient information of the control performance loss function is estimated via the output value after disturbance by the global optimization method of stochastic approximation algorithm. The PID controller parameter is iterative updated according to the gradient estimation value. Finally, we applied this method to the control process of cement grinding particle size, and the simulation results by MATLAB shown it can lead a high control precision which proves the feasibility and effectiveness of this adaptive PID control method.

The simultaneous perturbation stochastic approximation (SPSA) algorithm has recently attracted considerable attention for optimization problems where it is difficult or impossible to obtain a direct gradient of the objective (say, loss) function. The approach is based on a highly efficient simultaneous perturbation approximation to the gradient based on loss function measurements. SPSA is based on picking a simultaneous perturbation (random) vector in a Monte Carlo fashion as part of generating the approximation to the gradient. This paper derives the optimal distribution for the Monte Carlo process. The objective is to minimize the mean square error of the estimate. We also consider maximization of the likelihood that the estimate be confined within a bounded symmetric region of the true parameter. The optimal distribution for the components of the simultaneous perturbation vector is found to be a symmetric Bernoulli in both cases. We end the paper with a numerical study related to the area of experiment design.

The most widely used method applied in the context of off-line dynamic demand calibration is Simultaneous Perturbation Stochastic Approximation (SPSA). In the research following the SPSA approach single origin-destination (O-D) demand components were mostly considered as calibration parameters. However, basic SPSA, especially in high dimensions, shows convergence issues, as proven by various authors. To overcome this drawback, some authors suggested modifications of basic SPSA to improve its performance. In this paper, we investigate various techniques and approaches to improve the SPSA performance, and overcome, or at least alleviate, its shortcomings. We concentrate our analysis mostly on SPSA coefficients and gradient control. The comparison of investigated settings is conducted on a real-world network. This establishes a path to identify critical aspects that influence the calibration process and suggests an optimal SPSA configuration for practice. The contribution of this paper is to provide a detailed analysis of the SPSA behavior in cases its configuration is subject to various modifications. The findings are primarily intended for the offline context. However, the insights can also be used for the selection of the most efficient SPSA configuration given time constraint, particularly suitable for on-line applications.

Daniel C. ChinThe Johns Hopkins University AppliedPhysics Laboratory11100 Johns Hopkins RoadLaurel, Maryland 20723-6099E-mail: daniel.chin@jhuapl.eduAbstract. Extracting a multivariate nonlinear physical model from a setof satellite images is considered as a multivariate nonlinear regressionproblem. Multiple local solutions often prevent gradient type algorithmsfrom obtaining global optimal solutions. A method of solving this problemis presented based on the simultaneous perturbation stochastic approxi-mation (SPSA) algorithm. The method is applied to a problem of estimat-ing the distribution of energetic ion populations in the magnetospherefrom global images of the magnetosphere. The approach uses multipleobjective functions: single image errors and the summation of squareimage errors. The algorithm is demonstrated on simulated energetic-neutral atom (ENA) images. Within a reasonable number of functionevaluations, the process converges and reconstructs the images with amean square error less than or equal to 0.1% of the original image. Also,the SPSA method is compared with results obtained from simulated an-nealing (SAN) in a single objective function setting. In the comparisonstudy, SPSA has a 3:1 advantage over SAN in both accuracy and effi-ciency measures.

Fuzzy age-dependent replacement policy and SPSA algorithm based-on fuzzy simulation

Joint Calibration for DTA Model Using Islands-GA and PC-SPSA