• Computational Structural Engineering
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• v.8 no.2
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• pp.123-132
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• 1995

Genetic Algorithms(GA), which are based on the theory of natural evolution, have been evaluated highly for their robust performances. Traditional GA has mostly used binary code for representing design variable. The binary code GA has many difficulties to solve optimization problems with continuous design variables because of its large computer core memory size, inefficiency of its computing time, and its bad performance on local search. In this paper, a real code GA is proposed for dealing with the above problems. So, new crossover and mutation processes of GA are developed to use continuous design variables directly. The results of read code GA are compared with those of binary code GA for several single and multiple objective optimization problems. As a result of comparisons, it is found that the performance of the real code GA is better than that of the binary code GA, and concluded that the real code GA developed here can be used for the general optimization problem.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.04a
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• pp.581-586
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• 2009

This paper presents an optimal design of magnetorheological(MR) damper based on analytical methodology and finite element analysis. The proposed MR damper consists of MR valve and gas chamber. The MR valve is constrained in a specific volume and the optimization problem identifies geometric dimensions of the valve structure that maximize the pressure drop of the MR valve or damping force of the MR damper. In this work, the single-coil annular MR valve structure is considered. After describing the schematic configuration and operating principle of MR valve and damper, a quasi-static model is derived based on Bingham model of MR fluid. The magnetic circuit of the valve and damper is then analyzed by applying the Kirchoff’s law and magnetic flux conservation rule. Based on the quasi-static modeling and the magnetic circuit analysis, the optimization problem of the MR valve and damper is built. The optimal solution of the optimization problem of the MR valve structure constrained in a specific volume is then obtained and compared with the solution obtained from finite element method.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.11
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• pp.1110-1118
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• 2009

This paper presents an optimal design of magnetorheological(MR) damper based on analytical methodology and finite element analysis. The proposed MR damper consists of MR valve and gas chamber. The MR valve is constrained in a specific volume and the optimization problem identifies geometric dimensions of the valve structure that maximize the pressure drop of the MR valve or damping force of the MR damper. In this work, the single-coil annular MR valve structure is considered. After describing the schematic configuration and operating principle of MR valve and damper, a quasi-static model is derived based on Bingham model of MR fluid. The magnetic circuit of the valve and damper is then analyzed by applying the Kirchoff' s law and magnetic flux conservation rule. Based on the quasi-static modeling and the magnetic circuit analysis, the optimization problem of the MR valve and damper is built. The optimal solution of the optimization problem of the MR valve structure constrained in a specific volume is then obtained and compared with the solution obtained from finite element method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.3 s.258
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• pp.355-364
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• 2007

Multidisciplinary design optimization based on independent subspaces (MDOIS) is a simple and practical method that can be applied to the practical engineering MDO problems. However, the current version of MDOIS does not handle the common design variables. A new version of MDOIS is proposed and named as MDOIS/2006. It is a two-level MDO method while the original MDOIS is a single-level method. At first, system analysis is performed to solve the coupling in the analysis. If the termination criteria are not satisfied, each discipline solves its own design problem. Each discipline in the lower level solves the problem with common design variables while they are constrained by equality constraints. In the upper level, the common design variables of related disciplines are determined by using the optimum sensitivity of the objective function. To validate MDOIS/2006, mathematical problem and NASA test bed problem are solved. The results are compared with those from other MDO methods. Finally, MDOIS/2006 is applied to flow patterner design and shows that it can be successfully applied to the practical engineering MDO problem.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.11
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• pp.861-869
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• 1989

The optimization problems, based on Pontryagin's Maximum Principle, for minimizing (damping) Xenon spatial oscillations in Load Following operations of Pressurized Water Reactor (PWR) is presented. The optimization model is formulated as an optimal tracking problem with quadratic objective functional. The oen-group diffusion equations and Xe-I dynamic equations are defined as equality constraints. By applying the maximum principle, the original problem is decomposed into a single time problem with no constraints. The resultant subproblems are optimized by using the conjugate Gradient Method. The computational results show that the Xenon spatial oscillation is minimized, and the reactor follows the load demand of the electrical power systems while maintaining the desired power distribution.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.4
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• pp.268-274
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• 2013

In this paper, objective functions are defined for optimization of radar absorbing structures (RAS) on the aircraft wing leading edge. RAS is regarded as a single layer structure made of dielectrics. Design variables are the real and imaginary parts of complex permittivity. Reflection coefficient(RC) and radar cross section(RCS) are used in the objective function respectively. Transmission line theory is employed to calculate the RC. The RCS is evaluated by using physical optics(PO) for a leading edge part model. Genetic algorithm(GA) is used to perform optimization procedures. The radar absorbing performance of designed RAS is assessed by the RCS of a wing which has RAS on the leading edge.

• Knowledge Management Research
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• v.21 no.1
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• pp.27-40
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• 2020

Response surface methodology (RSM) empirically studies the relationship between a response variable and input variables in the product or process development phase. The ultimate goal of RSM is to find an optimal condition of the input variables that optimizes (maximizes or minimizes) the response variable. RSM can be seen as a knowledge management tool in terms of creating and utilizing data, information, and knowledge about a product production and service operations. In the field of product or process development, most real-world problems often involve a simultaneous consideration of multiple response variables. This is called a multiple response surface (MRS) problem. Various approaches have been proposed for MRS optimization, which can be classified into loss function approach, priority-based approach, desirability function approach, process capability approach, and probability-based approach. In particular, the loss function approach is divided into univariate and multivariate approaches at large. This paper focuses on the univariate approach. The univariate approach first obtains the mean square error (MSE) for individual response variables. Then, it aggregates the MSE's into a single objective function. It is common to employ the weighted sum or the Tchebycheff metric for aggregation. Finally, it finds an optimal condition of the input variables that minimizes the objective function. When aggregating, the relative weights on the MSE's should be taken into account. However, there are few studies on how to determine the weights systematically. In this study, we propose an interactive procedure to determine the weights through considering a decision maker's preference. The proposed method is illustrated by the 'colloidal gas aphrons' problem, which is a typical MRS problem. We also discuss the extension of the proposed method to the weighted MSE (WMSE).

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.39-45
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• 2008

This paper considers the multi-objective optimization of a multi-service arrayed waveguide grating-based single-hop WDM network with the two conflicting objectives of maximizing throughput while minimizing delay. This paper presents a genetic algorithm based methodology for finding the optimal throughput-delay tradeoff curve, the so-called Pareto-optimal frontier. Genetic algorithm based methodology provides the network architecture parameters and the Medium Access Control protocol parameters that achieve the Pareto-optima in a computationally efficient manner. The numerical results obtained with this methodology provide the Pareto-optimal network planning and operation solution for a wide range of traffic scenarios. The presented methodology is applicable to other networks with a similar throughput-delay tradeoff.

• Korean Chemical Engineering Research
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• v.56 no.1
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• pp.29-41
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• 2018

Separating a mixture having an azeotrope or low relative volatility with single distillation column is difficult. Separating water-acetic acid mixture and water-ethanol mixture with a distillation column consumes a lot of energy. Pervaporation membrane can be used to separate the mixture in the concentration region where separation is difficult with distillation. We simulated a distillation-membrane hybrid process where membrane is located on the head of the distillation column for efficient separation of water-acetic acid and water-ethanol mixture. Permeability data were obtained from experiments and literature. We formulated an optimization problem for the process with total annual cost (TAC) as an objective function and major design variables as optimization variables. Major optimization variable affecting TAC of the hybrid process was shown to be distillate concentration. We also suggested a simplified optimization procedure to get a close-to-optimal solution.

• Journal of Electrical Engineering and Technology
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• v.5 no.2
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• pp.179-190
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• 2010

This paper describes a new stochastic heuristic algorithm in engineering problem optimization especially in power system applications. An improved particle swarm optimization (PSO) called adaptive particle swarm optimization (APSO), mixed with simulated annealing (SA), is introduced and referred to as APSO-SA. This algorithm uses a novel PSO algorithm (APSO) to increase the convergence rate and incorporate the ability of SA to avoid being trapped in a local optimum. The APSO-SA algorithm efficiency is verified using some benchmark functions. This paper presents the application of APSO-SA to find the optimal location, type and size of flexible AC transmission system devices. Two types of FACTS devices, the thyristor controlled series capacitor (TCSC) and the static VAR compensator (SVC), are considered. The main objectives of the presented method are increasing the voltage stability index and over load factor, decreasing the cost of investment and total real power losses in the power system. In this regard, two cases are considered: single-type devices (same type of FACTS devices) and multi-type devices (combination of TCSC, SVC). Using the proposed method, the locations, type and sizes of FACTS devices are obtained to reach the optimal objective function. The APSO-SA is used to solve the above non.linear programming optimization problem for better accuracy and fast convergence and its results are compared with results of conventional PSO. The presented method expands the search space, improves performance and accelerates to the speed convergence, in comparison with the conventional PSO algorithm. The optimization results are compared with the standard PSO method. This comparison confirms the efficiency and validity of the proposed method. The proposed approach is examined and tested on IEEE 14 bus systems by MATLAB software. Numerical results demonstrate that the APSO-SA is fast and has a much lower computational cost.