• Journal of the Computational Structural Engineering Institute of Korea
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• v.26 no.1
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• pp.69-77
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• 2013

When structural design of a tall building is conducted, reduction of wind-induced lateral displacement is one of the most important problem. For this purpose, additional dampers and vibration control devices are generally considered. In this process, control performance of additional devices are usually investigated for optimal design without variation of characteristics of a structure. In this study, multi-objective integrated optimization of structure-smart control device is conducted and possibility of reduction of structural resources of a tall building with additional smart damping device has been investigated. To this end, a 60-story diagrid building structure is used as an example structure and artificial wind loads are used for evaluation of wind-induced responses. An MR damper is added to the conventional TMD to develop a smart TMD. Because dynamic responses and the amount of structural material and additional smart damping devices are required to be reduced, a multi-objective genetic algorithm is employed in this study. After numerical simulation, various optimal designs that can satisfy control performance requirement can be obtained by appropriately reducing the amount of structural material and additional smart damping device.

• Structural Engineering and Mechanics
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• v.44 no.3
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• pp.325-337
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• 2012

In this article, the multi-objective optimization of cylindrical aluminum tubes under axial impact load is presented. The specific absorbed energy and the maximum crushing force are considered as objective functions. The geometric dimensions of tubes including diameter, length and thickness are chosen as design variables. D/t and L/D ratios are constricted in the range of which collapsing of tubes occurs in concertina or diamond mode. The Non-dominated Sorting Genetic Algorithm-II is applied to obtain the Pareto optimal solutions. A back-propagation neural network is constructed as the surrogate model to formulate the mapping between the design variables and the objective functions. The finite element software ABAQUS/Explicit is used to generate the training and test sets for the artificial neural networks. To validate the results of finite element model, several impact tests are carried out using drop hammer testing machine.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.5
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• pp.445-454
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• 2015

In this paper, we propose an optimal design method for a dual reflector antenna(DRA) using the Genetic algorithm. In order to reduce the computational burden during the optimal design, we exploit the iterative physical optics(IPO) to calculate the surface current distribution at each reflector antenna. To improve the accuracy, we consider the shadow effect by the structure and the coupling effect by the multi-reflection based on the iterative MFIE(Magnetic Field Integral Equation). To reduce the number of design variables and generate a smooth surface, we use the Bezier function with the control points, which become the design variables in this paper. We adopt the HPBW(Half Power Beam Width), the FNBW(First Null Beam Width), and the SLL(Side Lobe Level) as the objective or cost functions. To verify the results, we compare them with the those of the commercial tool.

• Convergence Security Journal
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• v.17 no.1
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• pp.31-38
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• 2017

As the cyber-attacks through the networks advance, it is difficult for the intrusion detection system based on the simple rules to detect the novel type of attacks such as Advanced Persistent Threat(APT) attack. At present, many types of research have been focused on the application of machine learning techniques to the intrusion detection system in order to detect previously unknown attacks. In the case of using the machine learning techniques, the performance of the intrusion detection system largely depends on the feature set which is used as an input to the system. Generally, more features increase the accuracy of the intrusion detection system whereas they cause a problem when fast responses are required owing to their large elapsed time. In this paper, we present a network intrusion detection system based on artificial neural network, which adopts a multi-objective genetic algorithm to satisfy the both requirements: accuracy, and fast response. The comparison between the proposing approach and previously proposed other approaches is conducted against NSL_KDD data set for the evaluation of the performance of the proposing approach.

• Convergence Security Journal
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• v.18 no.5_1
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• pp.59-66
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• 2018

The performance of a network intrusion detection system using the machine learning method depends heavily on the composition and the size of the feature set. The detection accuracy, such as the detection rate or the false positive rate, of the system relies on the feature composition. And the time it takes to train and detect depends on the size of the feature set. Therefore, in order to enable the system to detect intrusions in real-time, the feature set to beused should have a small size as well as an appropriate composition. In this paper, we show that the size of the feature set can be further reduced without decreasing the detection rate through using Pearson correlation coefficient between features along with the multi-objective genetic algorithm which was used to shorten the size of the feature set in previous work. For the evaluation of the proposed method, the experiments to classify 10 kinds of attacks and benign traffic are performed against NSL_KDD data set.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.1
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• pp.121-129
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• 2022

With the recent increase in international trade volume the trade volume through ships is also continuously increasing. The treatment of ballast water goes through the following five steps, samples are taken and analyzed at each step, and samples are obtained using a suction pump. These suction pumps have low efficiency and thus need to be improved. In this study, it is to optimize the form of the impeller which affects directly improvements of performance to determine the capacity of suction pump and to fulfill the purpose of this research. To do it, we have carried out parametric design as an input variable, geometric form for the impeller. By conducting the flow analysis for the optimum form, it has confirmed the value of improved results and achieved the purpose to study in this paper. It has selected the necessary parameter for optimizing the form of the pump impeller and analyzed the property using experiment design. And it can reduce the factor of parameter for local optimization from findings to analyze the property of form parameter. To perform MOGA(Multi-Objective Genetic Algorithm) it has generated response surface using parameters for local optimization and conducts the optimization using multi-objective genetic algorithm. with created experiment cases, it has performed the computational fluid dynamics with model applying the optimized impeller form and checked that the capacity of the pump was improved. It could verify the validity concerning the improvement of pump efficiency, via optimization of pump impeller form which is suggested in this study.

• Industrial Engineering and Management Systems
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• v.11 no.3
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• pp.233-240
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• 2012

The implementation of business process management (BPM) systems in large number of business organizations transforms BPM system into such a level of maturity and tends to collect large repositories of business process (BP) models. This issue encourages BP flexibility that leads to a large number of process variants derived from the same model, but differing in structure, to be stored in the large repositories of BP models. Therefore, the repositories may include thousands of activities and related business objects with variation of requirements and quality of service. It is a common practice to customize processes from reference processes or templates in order to reduce the time and effort required to design and deploy processes on all levels. In order to address redundancy and underutilization problems, a generic process model, called as reference BP, is absolutely necessary to cover the best of process variants. This study aims to develop multiple-objective business process genetic algorithm (MOBPGA) to find a set of non-dominated (Pareto) solutions of business reference model to enhance conventional approach which considered only a single objective on creating BP reference model by using proximity score measurement. A mixed-integer linear program is constructed to evaluate performance of the proposed MOBPGA on small-scale problems by using standard measures for multiple-objective techniques. The results will show the viability of applying MOBPGA in terms of simultaneously maximizing proximity score measurement, minimizing total duration, and total costs of the selected reference model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.95-104
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• 2017

This paper deals with solution methods for discrete and multi-valued optimization problems. The objective function of the problem incorporates noise effects generated in case that fitness evaluation is accomplished by computer based experiments such as Monte Carlo simulation or discrete event simulation. Meta heuristics including Genetic Algorithm (GA) and Discrete Particle Swarm Optimization (DPSO) can be used to solve these simulation based multi-valued optimization problems. In applying these population based meta heuristics to simulation based optimization problem, samples size to estimate the expected fitness value of a solution and population (particle) size in a generation (step) should be carefully determined to obtain reliable solutions. Under realistic environment with restriction on available computation time, there exists trade-off between these values. In this paper, the effects of sample and population sizes are analyzed under well-known multi-modal and multi-dimensional test functions with randomly generated noise effects. From the experimental results, it is shown that the performance of DPSO is superior to that of GA. While appropriate determination of population sizes is more important than sample size in GA, appropriate determination of sample size is more important than particle size in DPSO. Especially in DPSO, the solution quality under increasing sample sizes with steps is inferior to constant or decreasing sample sizes with steps. Furthermore, the performance of DPSO is improved when OCBA (Optimal Computing Budget Allocation) is incorporated in selecting the best particle in each step. In applying OCBA in DPSO, smaller value of incremental sample size is preferred to obtain better solutions.

• Smart Structures and Systems
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• v.16 no.1
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• pp.1-26
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• 2015

Minimizing construction cost and reducing seismic damage are two conflicting objectives in the design of any new structure. In the present work, we try to develop a framework in order to solve the optimum performance-based design problem considering the construction cost and the seismic damage of steel moment-frame structures. The Park-Ang damage index is selected as the seismic damage measure because it is one of the most realistic measures of structural damage. The non-dominated sorting genetic algorithm (NSGA-II) is employed as the optimization algorithm to search the Pareto optimal solutions. To improve the time efficiency of the proposed framework, three simplifying strategies are adopted: first, simplified nonlinear modeling investigating minimum level of structural modeling sophistication; second, fitness approximation decreasing the number of fitness function evaluations; third, wavelet decomposition of earthquake record decreasing the number of acceleration points involved in time-history loading. The constraints of the optimization problem are considered in accordance with Federal Emergency Management Agency's (FEMA) recommended seismic design specifications. The results from numerical application of the proposed framework demonstrate the efficiency of the framework in solving the present multi-objective optimization problem.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1835-1844
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• 2022

Single-orifice plate is wildly utilized in the piping system of the nuclear power plant to throttle and depressurize the fluid of the pipeline. The cavitation induced by the single-orifice plate may cause some serious vibration of the pipeline. This study aims to find the optimal designs of the single-orifice plates that may have weak cavitation possibilities. For this purpose, a new single-orifice plate with a convergent-flat-divergent hole was modeled, a multi-objective optimization method was proposed to optimize the shape of a single-orifice plate, while computational fluid dynamics method was adopted to obtain the fluid physical quantities. The reciprocal cavitation number and the developmental integral were treated as cavitation indexes (e.g., objectives for the optimization algorithm). Two non-dominant designs ultimately achieved illustrated obvious reduction in the cavitation indexes at a Reynolds number Re = 1 ×10⁵ defined based on fluid velocity. Besides, the sensitivity analysis and temperature effects were also performed. The results indicated that the convergent angle of the single-orifice plate dominants the cavitation behavior globally. The optimal designs of single-orifice plates result in lower downstream jet areas and lower upstream pressure. For a constant Reynolds number, the higher temperature of liquid water, the easier it is to undergo cavitation. Whereas there is a diametric phenomenon for a constant fluid velocity. Moreover, the regression models were carried out to establish the mathematical relation between temperature and cavitation indexes.