• Journal of Korea Multimedia Society
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• v.20 no.7
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• pp.1090-1101
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• 2017

This study analyzes spatial density and integration of Space Syntax and Discrete Event Simulation (DEVS) of complex system theory and analyzes spatial structure by property, type and depth. The aim of this study is to secure the validity of the theoretical application. The study evaluated the correlation between spatial density and integration by setting up eight types of analysis models. In addition, analyzed the correlation of structural characteristics and approached the application of discrete event simulation of spatial syntax theory. It is confirmed that the concept of integration of spatial syntax theory and analysis using discrete event simulation are valid as new spatial analysis methodology. Also expect that realistic and concrete predictions will be possible if discrete event simulation evolves into research for space allocation and space efficiency optimization.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.5
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• pp.1-7
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• 2008

This paper presents not only the robust stabilization technique but also robust non-fragile controller design method for discrete-time singular systems and static state feedback controller with multiplicative uncertainty. The condition for the existence of robust stabilization controller, the admissible controller design method, and the measure of non-fragility in controller are proposed via LMI(linear matrix inequality) approach. In order to get the maximum measure of non-fragility, the obtained sufficient condition can be rewritten as LMI optimization form in terms of transformed variable. Therefore, the presented robust non-fragile controller for discrete-time singular systems guarantees robust stability in spite of parameter uncertainty and controller fragility. Finally, a numerical example is given to show the validity of the design method.

• Steel and Composite Structures
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• v.24 no.1
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• pp.93-112
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• 2017

Metaheuristic algorithms in general make use of uniform random numbers in their search for optimum designs. Levy Flight (LF) is a random walk consisting of a series of consecutive random steps. The use of LF instead of uniform random numbers improves the performance of metaheuristic algorithms. In this study, three discrete optimum design algorithms are developed for steel skeletal structures each of which is based on one of the recent metaheuristic algorithms. These are biogeography-based optimization (BBO), brain storm optimization (BSO), and artificial bee colony optimization (ABC) algorithms. The optimum design problem of steel skeletal structures is formulated considering LRFD-AISC code provisions and W-sections for frames members and pipe sections for truss members are selected from available section lists. The minimum weight of steel structures is taken as the objective function. The number of steel skeletal structures is designed by using the algorithms developed and effect of LF is investigated. It is noticed that use of LF results in up to 14% lighter optimum structures.

• Journal of Navigation and Port Research
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• v.30 no.2
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• pp.137-143
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• 2006

The optimum grillage design belongs to nonlinear constrained optimization problem. The determination of beam scantlings for the grillage structure is a very crucial matter out of whole structural design process. The performance of optimization methods, based on penalty functions, is highly problem-dependent and many methods require additional tuning of some variables. This additional tuning is the influences of penalty coefficient, which depend strongly on the degree of constraint violation. Moreover, Binary-coded Genetic Algorithm (BGA) meets certain difficulties when dealing with continuous and/or discrete search spaces with large dimensions. With the above reasons, Real-coded Micro-Genetic Algorithm ($R{\mu}GA$) is proposed to find the optimum beam scantlings of the grillage structure without handling any of penalty functions. $R{\mu}GA$ can help in avoiding the premature convergence and search for global solution-spaces, because of its wide spread applicability, global perspective and inherent parallelism. Direct stiffness method is used as a numerical tool for the grillage analysis. In optimization study to find minimum weight, sensitivity study is carried out with varying beam configurations. From the simulation results, it has been concluded that the proposed $R{\mu}GA$ is an effective optimization tool for solving continuous and/or discrete nonlinear real-world optimization problems.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.8
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• pp.921-927
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• 2012

Reliability-based design optimization (RBDO) can be used to determine the reliability of a system by means of probabilistic design criteria, i.e., the possibility of failure considering stochastic features of design variables and input parameters. To assure these criteria, various reliability analysis methods have been developed. Most of these methods assume that distribution functions are continuous. However, in real problems, because real data is often discrete in form, it is important to estimate the distributions for discrete information during reliability analysis. In this study, we employ the Akaike information criterion (AIC) method for reliability analysis to determine the best estimated distribution for discrete information and we suggest an RBDO method using AIC. Mathematical and engineering examples are illustrated to verify the proposed method.

• Journal of Korean Society of Steel Construction
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• v.13 no.6
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• pp.673-681
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• 2001

The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithm. The algorithm can perform both shape and topology optimum designs of trusses. The developed algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of trusses and the constraints are stress and displacement. The basic search method for the optimum design is the genetic algorithm. The algorithm is known to be very efficient for the discrete optimization. The genetic algorithm consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the design points selected form the genetic process. The evolutionary process evaluates the survivability of the design points. The evolutionary process evaluates the survivability of the design points selected form the genetic process. The efficiency and validity of the developed size, shape and topology discrete optimum design algorithm was verified by applying the algorithm to optimum design examples.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.227-228
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• 2009

• Journal of Korean Institute of Industrial Engineers
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• v.13 no.1
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• pp.61-67
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• 1987

A tool for optimizing simulated discrete variable stochastic systems, SIMICOM was developed and presented in [5]. In this paper an evaluation of its performance and results of comparisons with other popular methods for dealing with simulation-optimization problems will be provided. Based on several test problems it is concluded that SIMICOM dominates those methods.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.858-863
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• 2004

In the optimized design of an actual structure, the design variable should be selected among any certain values or corresponds to a discrete design variable that needs to handle the size of a pre-formatted part. Various algorithms have been developed for discrete design. As recently reported, the sequential algorithm with orthogonal arrays(SOA), which is a local minimum search algorithm in discrete space, has excellent local minimum search ability. It reduces the number of function evaluation using orthogonal arrays. However it only finds a local minimum and the final solution depends on the initial value. In this research, the genetic algorithm, which defines an initial population with the potential solution in a global space, is adopted in SOA. The new algorithm, sequential algorithm with orthogonal arrays and genetic algorithm(SOAGA), can find a global solution with the properties of genetic algorithm and the solution is found rapidly with the characteristics of SOA.

• Computational Structural Engineering
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• v.26 no.1
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• pp.52-59
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• 2013