• Journal of the Korean Operations Research and Management Science Society
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• v.9 no.2
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• pp.46-55
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• 1984

The purpose of this parer is to provide a survey of the state of the art of routing methods in store-and-forward computer networks. The survey is carried out in line with a new taxonomy： heuristic methods, user-optimization methods, and system-optimization methods. This taxonomy on routing algorithms is based on two viewpoints： the level of optimization and the relative difficulty for the implementation in real computer networks. Some actual methods implemented in real computer networks are surveyed as well as the theoretical studies in the literature. This paper concludes with some points in need of further researches.

• Steel and Composite Structures
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• v.42 no.6
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• pp.855-864
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• 2022

Due to limited resources, and increasing speed of development, the optimal use of available resources has become the most important challenge of human societies. In the last few decades, many researchers have focused their research on solving various optimization problems, providing new optimization methods, and improving the performance of existing optimization methods. Echolocation Search Algorithm (ESA) is an evolutionary optimization algorithm that is based on mimicking the mechanism of the animals such as bats, dolphins, oilbirds, etc in food finding to solve optimization problems. In this paper, the ability of ESA for solving truss size optimization problems with continuous variables is investigated. To examine the efficiency of ESA, three benchmark examples are considered. The numerical results exhibit the effectiveness of ESA for solving truss optimization problems.

• Proceedings of the KIEE Conference
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• 2001.07e
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• pp.7-12
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• 2001

A large number of methods for the design optimization have been proposed in recent years. However, it is not easy to apply these methods to practical use because of many iterations. So, in the design optimization, physical and engineering investigation of the given model are very important, which results in an overall increase in the optimization speed. This paper describes a novel optimization procedure utilizing the conformal transformation method. This approach consists of two phases and has the advantage of grasping the physical phenomena of the model easily. Some numerical results that demonstrate the validity of the proposed method are also presented.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.90-98
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• 2015

In this paper, an advanced teaching-learning based optimization(TLBO) method for the magnetic levitation controller of Maglev transportation system is proposed to optimize the control performances. An attraction-type levitation system is intrinsically unstable and requires a delicate control. It is difficult to completely satisfy the desired performance through the methods using conventional methods and intelligent optimizations. In the paper, we use TLBO and clonal selection algorithm to choose the optimal control parameters for the magnetic levitation controller. To verify the proposed algorithm, we compare control performances of the proposed method with the genetic algorithm and the particle swarm optimization. The simulation results show that the proposed method is more effective than conventional methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.1972-1986
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• 2017

With the advent of the knowledge economy, science and technology resources have played an important role in economic competition, and their optimal allocation has been regarded as very important across the world. Thus, allocation optimization research for regional science and technology resources is significant for accelerating the reform of regional science and technology systems. Regional science and technology resource allocation optimization is modeled as a double-layer optimization model: the entire system is characterized by top-layer optimization, whereas the subsystems are characterized by bottom-layer optimization. To efficaciously solve this optimization problem, we propose a mixed search method based on the orthogonal genetic algorithm and sensitivity analysis. This novel method adopts the integrated modeling concept with a combination of the knowledge model and heuristic search model, on the basis of the heuristic search model, and simultaneously highlights the effect of the knowledge model. To compare the performance of different methods, five methods and two channels were used to address an application example. Both the optimized results and simulation time of the proposed method outperformed those of the other methods. The application of the proposed method to solve the problem of entire system optimization is feasible, correct, and effective.

• Korean System Dynamics Review
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• v.13 no.2
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• pp.47-71
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• 2012

The purpose of this research is the examination of validity of data as well as simulation model, i.e. to simulate the real data in the SD model with the least error using the adjustments for the faithful reflection of real data to the simulation. In general, SD programs (e.g. VENSIM) utilize the Euler or Runge-Kutta method as an algorithm. It is possible to reflect the trend of real data via these two estimation methods however can cause the validity problem in case of the simulation requiring the accuracy as they have endogenous errors. In this article, the future population estimated by the Korea National Statistical Office (KNSO) to 2050 is simulated by the aging chain model, dividing the population into three cohorts, 0-14, 15-64, 65 and over cohorts by age and offering the adjustments to them. Adjustments are calculated by optimization with three different methods, optimization in EXCEL, manual optimization with iterative calculation, and optimization in VENSIM DSS, the results are compared, and at last the optimal adjustment set with the least error are found among them. The simulation results with the pre-determined optimal adjustment set are validated by methods proposed by Barlas (1996) and other alternative methods. It is concluded that the result of simulation model in this research has no significant difference from the real data and reflects the real trend faithfully.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.822-827
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• 2005

Recently engineering systems problems become quite large and complicated. For those problems, design requirements are fairly complex. It is not easy to design such systems by considering only one discipline. Therefore, we need a design methodology that can consider various disciplines. Multidisciplinary Design Optimization (MDO) is an emerging optimization method to include multiple disciplines. So far, about seven MDO methodologies have been proposed for MDO. They are Multidisciplinary Feasible (MDF), Individual Feasible (IDF), All-at-Once (AAO), Concurrent Subspace Optimization (CSSO), Collaborative Optimization (CO), Bi-Level Integrated System Synthesis (BLISS) and Multidisciplinary Optimization Based on Independent Subspaces (MDOIS). In this research, the performances of the methods are evaluated and compared. Practical engineering problems may not be appropriate for fairness. Therefore, mathematical problems are developed for the comparison. Conditions for fair comparison are defined and the mathematical problems are defined based on the conditions. All the methods are coded and the performances of the methods are compared qualitatively as well as quantitatively.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.5
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• pp.342-350
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• 2013

Dual response surface optimization (DRSO), inspired by Taguchi's philosophy, attempts to optimize the process mean and variability by using response surface methodology. Researches on DRSO were extensively done in 1990's and have been matured recently. This paper reviews the existing DRSO methods from the decision making perspective. More specifically, this paper classifies the existing DRSO methods based on the optimization criterion and the timing of preference articulation. Also, some of case studies are reviewed. Extension to multiresponse optimization, triple response surface optimization, and application of data mining method are suggested as future research issues.

• 한국전산유체공학회:학술대회논문집
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• 2004.10a
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• pp.187-191
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• 2004

The conventional reliability based design optimization(RBDO) methods require high computational cost compared with the deterministic design optimization(DO) methods. To overcome the computational inefficiency of RBDO, single loop methods have been proposed. These need less function calls than that of RBDO but much more than that of DO. In this study, the approximate reliability method is proposed that the computational requirement is nearly the same as DO and the reliability accuracy is good compared with that of RBDO. Using this method, the 3-D viscous aerodynamic shape design optimization with uncertainty is performed very efficiently.

• Transactions of the Society of Information Storage Systems
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• v.3 no.4
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• pp.178-182
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• 2007

Topology optimization methods are classified into two methods such as the density method and the homogenization method. Those methods need to consider relationships between the material property and the density of each element in a design domain, the relaxation of the design space, etc. However, it is hard to apply on some cases due to the complexity to compose the design objective and its sensitivity analysis. In this paper, a modified topology optimization is proposed to assist designers who do not have mathematical or theoretical background of the topology optimization. In this study, optimal topology of structures can be achieved by the sequential design of experiment (DOE) and the sensitivity analysis. We conducted the DOE with an orthogonal array and the sensitivity analysis of design variables to determine sensitive variables used for connectivity between elements. The modified topology optimization method has advantages such as freedom from penalizing intermediate values and easy application with basic DOE concept.