• 한국공간구조학회논문집
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• 제10권4호
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• pp.93-102
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• 2010

본 연구에서는 MR 감쇠기와 FPS를 사용하여 구성된 스마트 스카이브릿지를 제안하였으며 스마트 스카이브릿지로 연결된 인접건물의 지진응답 제어성능을 분석하였다. 이를 위하여 스마트 스카이브릿지로 연결된 10층과 20층 구조물을 예제 구조물로 선택하였고 근거리 (near fault) 및 원거리 (far fault) 지진의 특성을 가지는 El Centro 지진과 Kobe지진을 사용하여 시간이력해석을 수행하였다. 스마트 스카이브릿지블 효과적으로 제어하기 위해서 퍼지논리제어기를 개발하였으며 퍼지논리제어기를 최적화하기 위하여 다목적 유전자알고리즘을 사용하였다. 최적화결과 10층 건물의 지진응답과 20층 건물의 지진응답 사이에는 상충관계 (trade-off)가 있는 것을 알 수 있었고 다목적 유전자알고리즘을 통해서 두 건물의 지진응답 제어에 대한 퍼지논리제어거의 파레토 해집합을 구할 수 있었다. 수치해석결과 본 연구에서 제안한 스마트 스카이브릿지를 사용하면 연결된 건물의 지진응답을 효율적으로 저감시킬 수 있는 것을 알 수 있었다.

• 대한토목학회논문집
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• 제28권2A호
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• pp.223-231
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• 2008

이 논문에서는 교량의 유지보수비용을 최소화할 뿐만 아니라 교량의 성능을 동시에 최대화할 수 있는 새로운 유지보수계획법을 제시한다. 교량 수명연한 동안의 유지보수비용과 교량의 바닥판, 주형, 하부구조의 상태등급으로 표현되는 교량의 성능을 동시에 최적화 하는 다목적 최적화 문제를 구성하여 최적의 유지보수계획을 수립한다. 다목적 최적화문제의 해를 얻기 위한 수치해석 방법으로 유전자 알고리즘(Genetic Algorithm, GA)을 사용하고, 다목적 최적화방법을 적용하여 얻어진 여러 개의 해집합 중 최적해의 선택을 위한 의사결정(decision making)을 위해 선호도기반 최적화방법을 적용한다. 일반적인 5경간의 PSC I형 교량에 대한 수치예제를 통해, 이 연구에서 제안하는 방법이 유지보수비용 및 교량성능간의 균형 있는 최적화를 이룰 수 있음을 보인다.

• 대한안전경영과학회지
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• 제11권3호
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• pp.131-138
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• 2009

This paper considers a coordinated scheduling problem between multi-suppliers and an manufacture. When the supplier has insufficient inventory to meet the manufacture's order, the supplier may use the expedited production and the expedited transportation. In this case, we consider a scheduling problem to minimize the total cost of suppliers and manufacture. We suggest an population management genetic algorithm with local search and crossover (GALPC). By the computational experiments comparing with general genetic algorithm, the objective value of GALPC is reduced by 8% and the calculation time of GALPC is reduced by 70%.

• 한국CDE학회논문집
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• 제15권1호
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• pp.1-10
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• 2010

Recently, a shipyard is making every effort to efficiently manage equipments of resources such as a gantry crane, transporter, and so on. So far block lifting scheduling of a gantry crane has been manually performed by a manager of the shipyard, and thus it took much time to get scheduling results and moreover the quality of them was not optimal. To improve this, a block lifting scheduling system of the gantry crane using optimization techniques was developed in this study. First, a block lifting scheduling problem was mathematically formulated as a multi-objective optimization problem, considering the minimization of travel distance at an idle state and wire replacement during block lifting. Then, to solve the problem, a meta-heuristic optimization algorithm based on the genetic algorithm was proposed. To evaluate the efficiency and applicability of the developed system, it was applied to an actual block lifting scheduling problem of the shipyard. The result shows that blocks can be efficiently lifted by the gantry crane using the developed system, compared to manual scheduling by a manager.

• Earthquakes and Structures
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• 제7권3호
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• pp.271-294
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• 2014

In recent years, along with the advances made in performance-based design optimization, the need for fast calculation of response parameters in dynamic analysis procedures has become an important issue. The main problem in this field is the extremely high computational demand of time-history analyses which may convert the solution algorithm to illogical ones. Two simplifying strategies have shown to be very effective in tackling this problem; first, simplified nonlinear modeling investigating minimum level of structural modeling sophistication, second, wavelet analysis of earthquake records decreasing the number of acceleration points involved in time-history loading. In this paper, we try to develop an efficient framework, using both strategies, to solve the performance-based multi-objective optimal design problem considering the initial cost and the seismic damage cost of steel moment-frame structures. The non-dominated sorting genetic algorithm (NSGA-II) is employed as the optimization algorithm to search the Pareto optimal solutions. The constraints of the optimization problem are considered in accordance with Federal Emergency Management Agency (FEMA) recommended design specifications. The results from numerical application of the proposed framework demonstrate the capabilities of the framework in solving the present multi-objective optimization problem.

• Industrial Engineering and Management Systems
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• 제15권4호
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• pp.374-384
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• 2016

The efficiency of a supply chain can be extremely affected by its design which includes determining the flow pattern of material from suppliers to costumers, selecting the suppliers, and defining the opened facilities in network. In this paper, a multi-objective multi-echelon multi-product supply chain design model is proposed in which several suppliers, several manufacturers, several distribution centers as different stages of supply chain cooperate with each other to satisfy various costumers' demands. The multi-objectives of this model which considered simultaneously are 1-minimize the total cost of supply chain including production cost, transportation cost, shortage cost, and costs of opening a facility, 2-minimize the transportation time from suppliers to costumers, and 3-maximize the service level of the system by minimizing the maximum level of shortages. To configure this model a graph theoretic approach is used by considering channels among each two facilities as links and each facility as the nodes in this configuration. Based on complexity of the proposed model a multi-objective Pareto-based vibration damping optimization (VDO) algorithm is applied to solve the model and finally non-dominated sorting genetic algorithm (NSGA-II) is also applied to evaluate the performance of MOVDO. The results indicated the effectiveness of the proposed MOVDO to solve the model.

• 한국지능시스템학회논문지
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• 제12권6호
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• pp.491-496
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• 2002

다목적 함수 최적화 문제(Multi-objective Optimization Problems : MOPs)는 공학적인 문제를 풀고자 할 때 자주 접하게 되는 대표적인 문제 중 하나이다. 공학자들이 다루는 실세계 최적화 문제들은 몇 개의 경합하는 목적 함수(objective function) 들로 이루어진 문제일 경우가 많다. 본 논문에서는 다목적 함수 최적화 문제의 정의를 소개하고 이 문제를 풀기 위한 몇 가지 접근법을 소개한다. 먼저 서론에서는 파레토 최적해(Pareto optimal solution) 의 개념을 이용한 기존의 최적화 알고리즘과 이와는 달리 게임 이론(Game Theory) 으로부터 도출된 최적화 알고리즘인 내쉬 유전자 알고리즘(Nash Genetic Algorithm Nash GA) 그리고 본 논문에서 제안하는 공진화 알고리즘의 기반이 되는 진화적 안정 전략 (Evolutionary Stable Strategy : ESS) 의 이론적 배경을 소개한다. 또 본론에서는 다목적 함수 최적화 문제와 파레토 최적 해의 정의를 소개하고 다목적 함수 최적화 문제를 풀기 위하여 유전자 알고리즘을 진화적 게임 이론(Evolutionary Game Theory : EGT) 에 적용시킨 내쉬 유전자 알고리즘과 본 논문에서 새로이 제안하는 공진화 알고리즘의 구조를 설명하고 이 두 가지 알고리즘을 대표적인 다목적 함수 최적화 문제에 적용하고 결과를 비교 검토함으로써 진화적 게임 이론의 두 가지 아이디어 내쉬의 균형(Equilibrium) 과 진화적 안정전략 에 기반한 최적화 알고리즘들이 다목적 함수 문제의 최적해 를 탐색할 수 있음을 확인한다.

• International Journal of Naval Architecture and Ocean Engineering
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• 제7권4호
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• pp.750-769
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• 2015

The geometry of engineering systems affects their performances. For this reason, the shape of engineering systems needs to be optimized in the initial design stage. However, engineering system design problems consist of multi-objective optimization and the performance analysis using commercial code or numerical analysis is generally time-consuming. To solve these problems, many engineers perform the optimization using the approximation model (response surface). The Response Surface Method (RSM) is generally used to predict the system performance in engineering research field, but RSM presents some prediction errors for highly nonlinear systems. The major objective of this research is to establish an optimal design method for multi-objective problems and confirm its applicability. The proposed process is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the approximation model is generated using the Backpropagation Artificial Neural Network (BPANN) which is considered as Neuro-Response Surface Method (NRSM). The optimization is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case studies of marine system and ship structure (substructure of floating offshore wind turbine considering hydrodynamics performances and bulk carrier bottom stiffened panels considering structure performance), we have confirmed the applicability of the proposed method for multi-objective side constraint optimization problems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권3호
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• pp.755-778
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• 2024

In recent years, the number of devices being connected to the internet has grown enormously, as has the intrusive behavior in the network. Thus, it is important for intrusion detection systems to report all intrusive behavior. Using deep learning and machine learning algorithms, intrusion detection systems are able to perform well in identifying attacks. However, the concern with these deep learning algorithms is their inability to identify a suitable network based on traffic volume, which requires manual changing of hyperparameters, which consumes a lot of time and effort. So, to address this, this paper offers a solution using the extended compact genetic algorithm for the automatic tuning of the hyperparameters. The novelty in this work comes in the form of modeling the problem of identifying attacks as a multi-objective optimization problem and the usage of linkage learning for solving the optimization problem. The solution is obtained using the feature map-based Convolutional Neural Network that gets encoded into genes, and using the extended compact genetic algorithm the model is optimized for the detection accuracy and latency. The CIC-IDS-2017 and 2018 datasets are used to verify the hypothesis, and the most recent analysis yielded a substantial F1 score of 99.23%. Response time, CPU, and memory consumption evaluations are done to demonstrate the suitability of this model in a fog environment.

• Steel and Composite Structures
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• 제44권2호
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• pp.211-224
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• 2022

Multi-objective optimization was conducted to obtain the optimal configuration of a composite sandwich structure with honeycomb-foam hybrid core subjected to underwater shock waves, which can fulfill the demand for light weight and energy efficient design of structures against underwater blast. Effects of structural parameters on the dynamic response of the sandwich structures subjected to underwater shock waves were analyzed numerically, from which the correlations of different parameters to the dynamic response were determined. Multi-objective optimization of the structure subjected to underwater shock waves of which the initial pressure is 30 MPa was conducted based on surrogate modelling method and genetic algorithm. Moreover, optimization results of the sandwich structure subjected to underwater shock waves with different initial pressures were compared. The research can guide the optimal design of composite sandwich structures subjected to underwater shock waves.