• The Transactions of the Korean Institute of Electrical Engineers
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• v.31 no.7
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• pp.18-24
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• 1982

We derived an optimal control of the Stochastic Bilinear Systems. For that we, firstly, formulated stochastic bilinear system and estimated its state when the system state is not directly observable. Optimal control problem of this system is reviewed on the line of three optimization techniques. An optimal control is derived using Hamilton-Jacobi-Bellman equation via dynamic programming method. It consists of combination of linear and quadratic form in the state. This negative feedback control, also, makes the system stable as far as value function is chosen to be a Lyapunov function. Several other properties of this control are discussed.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.3
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• pp.23-36
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• 2016

We consider a model that minimizes the total cost incurred by assigning available weapons to existing targets in order to reduce enemy threats, which is called the weapon target assignment problem (WTAP). This study addresses the stochastic versions of WTAP, in which data, such as the probability of destroying a target, are given randomly (i.e., data are identified with certain probability distributions). For each type of random data or parameter, we provide a stochastic optimization model on the basis of the expected value or scenario enumeration. In particular, when the probabilities of destroying targets depending on weapons are stochastic, we present a stochastic programming formulation with a simple recourse. We show that the stochastic model can be transformed into a deterministic equivalent mixed integer programming model under a certain discrete probability distribution of randomness. We solve the stochastic model to obtain an optimal solution via the mixed integer programming model and compare this solution with that of the deterministic model.

• Wind and Structures
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• v.9 no.6
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• pp.441-455
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• 2006

A more applicable optimization model for extracting flutter derivatives of bridge decks is presented, which is suitable for time-varying weights for fitting errors and different lengths of vertical bending and torsional free vibration data. A stochastic search technique for searching the optimal solution of optimization problem is developed, which is more convenient in understanding and programming than the alternate iteration technique, and testified to be a valid and efficient method using two numerical examples. On the basis of the section model test of Sutong Bridge deck, the flutter derivatives are extracted by the stochastic search technique, and compared with the identification results using the modified least-square method. The Empirical Mode Decomposition method is employed to eliminate noise, trends and zero excursion of the collected free vibration data of vertical bending and torsional motion, by which the identification precision of flutter derivatives is improved.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.9
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• pp.33-44
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• 2006

As many scientific phenomena are now investigated using complex computer models, the effective use of Kriging on physical problems has been expanded to provide global approximations for optimization problems. This paper is focused on the two types of strategies to improve efficiency and accuracy of approximate optimization models using Kriging. These methods are performed by the stochastic process, stochastic-localization method(SLM), as the criterion to move the local domains and the design of experiments(DOE), the classical design and space-filling design. The proposed methodology is applied to the designs of 3-bar truss, Sandgren's pressure vessel, and honeycomb upper platform of a satellite structure.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.4
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• pp.292-297
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• 2016

The first-order plus dead time(FOPDT) and second-order plus dead time(SOPDT), which describes a linear monotonic process quite well in most chemical and industrial processes and is often sufficient for PID and IMC controller tuning. This paper presents an application of heuristic harmony search(HS) optimization algorithm to the identification of linear continuous time-delay systems from step response. This recently developed HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. The effectiveness of the proposed identification method has been demonstrated through a number of simulation examples.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1992.04a
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• pp.73-78
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• 1992

A stochastic simulated anneal ins (SSA) is a recent approach to the solution of problems characterized by large number of interacting degrees of freedom. SSA simulates the degrees of freedom in a problem in a such a way that they are a collection of atoms slowly being coolded into a ground state which would correspond to the stationary point of the problem. In this paper, for a randomly disturbed current design, SSA optimization technique is used, which establishes a probabilistic criterion for acceptance or rejection of current design and iteratively improves it to arrive at a stationary Point at which critical temperature is reached. Simple truss optimization problem which consider as their constraints only the tensile and compressive yielding strength of the members are tested using SSA. Satisfactory results are obtained and some discussions are given for the behavior of SSA on the tested truss structures.

• Computational Structural Engineering
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• v.5 no.2
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• pp.93-104
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• 1992

The final objective of optimization methods is to find global optimum accurately and efficiently. The optmization processes by simulated annealing and genetic algorithm which have stochastic search process are examined and are applied to several mathematical models and truss, beam structures. Then the robustnesses of these two methods are studied and compared with the results of deterministic optimization methods from the viewpoints of reliability and running time in obtaining the global optimum.

• Industrial Engineering and Management Systems
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• v.13 no.2
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• pp.210-220
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• 2014

This article describes a 2-in-1 methodology utilizing simulation optimization technique and Data Envelopment Analysis in measuring an accurate efficiency score. Given the high level of stochastic data in real environment, a novel methodology known as Data Collection Budget Allocation-Data Envelopment Analysis (DCBA-DEA) is developed. An example of the method application is shown in banking institutions. In addition to the novel approach presented, this article provides a new insight to the application domain of efficiency measurement as well as the way one conducts efficiency study.

• Structural Engineering and Mechanics
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• v.56 no.6
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• pp.959-982
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• 2015

In this study, a non-stationary random earthquake Clough-Penzien model is used to describe earthquake ground motion. Using stochastic direct integration in combination with an equivalent linear method, a solution is established to describe the non-stationary response of lead-rubber bearing (LRB) system to a stochastic earthquake. Two parameters are used to develop an optimization method for bearing design: the post-yielding stiffness and the normalized yield strength of the isolation bearing. Using the minimization of the maximum energy response level of the upper structure subjected to an earthquake as an objective function, and with the constraints that the bearing failure probability is no more than 5% and the second shape factor of the bearing is less than 5, a calculation method for the two optimal design parameters is presented. In this optimization process, the radial basis function (RBF) response surface was applied, instead of the implicit objective function and constraints, and a sequential quadratic programming (SQP) algorithm was used to solve the optimization problems. By considering the uncertainties of the structural parameters and seismic ground motion input parameters for the optimization of the bearing design, convex set models (such as the interval model and ellipsoidal model) are used to describe the uncertainty parameters. Subsequently, the optimal bearing design parameters were expanded at their median values into first-order Taylor series expansions, and then, the Lagrange multipliers method was used to determine the upper and lower boundaries of the parameters. Moreover, using a calculation example, the impacts of site soil parameters, such as input peak ground acceleration, bearing diameter and rubber shore hardness on the optimization parameters, are investigated.

• Structural Engineering and Mechanics
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• v.25 no.1
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• pp.21-37
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• 2007

In this study a stochastic approach for linear viscous dampers design adopted for seismic protection of buildings is developed. Devices optimal placement into the main structure and their mechanical parameters are attained by means of a reliability-based optimum design criterion, in which an objective function (O.F.) is minimized, subject to a stochastic constraint. The seismic input is modelled by a non stationary modulated Kanai Tajimi filtered stochastic process. Building is represented by means of a plane shear type frame model. The selected criterion for the optimization searches the minimum of the O.F., here assumed to be the cost of the seismic protection, i.e., assumed proportional to the sum of added dampings of each device. The stochastic constraint limits a suitable approximated measure of the structure failure probability, here associated to the maximum interstorey drift crossing over a given threshold limit, related, according with modern Technical Codes, to the required damage control.