• Geomechanics and Engineering
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• v.30 no.6
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• pp.551-564
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• 2022

Fragmenting the rock mass is considered as the most important work in open-pit mines. Ground vibration is the most hazardous issue of blasting which can cause critical damage to the surrounding structures. This paper focuses on developing an explicit model to predict the ground vibration through an multi objective evolutionary polynomial regression (MOGA-EPR). To this end, a database including 79 sets of data related to a quarry site in Malaysia were used. In addition, a gene expression programming (GEP) model and several empirical equations were employed to predict ground vibration, and their performances were then compared with the MOGA-EPR model using the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²) and a20-index. Comparing the results, it was found that the MOGA-EPR model predicted the ground vibration more precisely than the GEP model and the empirical equations, where the MOGA-EPR scored lower MAE and RMSE, 𝜇 and 𝜎 closer to the optimum value, and higher R² and a20-index. Accordingly, the proposed MOGA-EPR model can be introduced as a useful method to predict ground vibration and has the capacity to be generalized to predict other blasting effects.

• Structural Engineering and Mechanics
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• v.63 no.4
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• pp.429-438
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• 2017

Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multi-objective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLP-FFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.4
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• pp.31-41
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• 2003

This paper deals with a cell formation problem for a set of m-machines and n-processing parts. Generally, a cell formation problem is known as NP-completeness. Hence the cell formation problem with multiple objectives is more difficult than single objective problem. The paper considers multiple objectives; minimize number of intercell movements, minimize intracell workload variation and minimize intercell workload variation. We propose a multiple objective genetic algorithms(MOGA) resolving the mentioned three objectives. The MOGA procedure adopted Pareto optimal solution for selection method for next generation and the concept of Euclidean distance from the ideal and negative ideal solution for fitness test of a individual. As we consider several weights, decision maker will be reflected his consideration by adjusting high weights for important objective. A numerical example is given for a comparative analysis with the results of other research.

• IE interfaces
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• v.20 no.4
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• pp.504-514
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• 2007

The multicast routing problem lies in the composition of a multicast routing tree including a source node and multiple destinations. There is a trade-off relationship between cost and delay, and the multicast routing problem of optimizing these two conditions at the same time is a difficult problem to solve and it belongs to a multi-objective optimization problem (MOOP). A multi-objective genetic algorithm (MOGA) is efficient to solve MOOP. A micro-genetic algorithm(${\mu}GA$) is a genetic algorithm with a very small population and a reinitialization process, and it is faster than a simple genetic algorithm (SGA). We propose a multi-objective micro-genetic algorithm (MO${\mu}GA$) that combines a MOGA and a ${\mu}GA$ to find optimal solutions (Pareto optimal solutions) of multicast routing problems. Computational results of a MO${\mu}GA$ show fast convergence and give better solutions for the same amount of computation than a MOGA.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2010.04a
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• pp.331-334
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• 2010

본 논문에서는 준능동 TMD가 설치된 고층건물의 풍응답을 효과적으로 저감시키기 위하여 다목적 유전자알고리즘(MOGA)을 이용한 퍼지관리제어기를 개발하였다. 퍼지관리제어기는 하위제어기인 그라운드훅(groundhook) 제어알고리즘과 스카이훅(skyhook) 제어알고리즘에 의해서 결정된 제어명령을 적절하게 하나로 합치는 역할을 한다. 다목적 유전자알고리즘의 최적화 과정에서 75층의 가속도 응답과 준능동 TMD의 변위응답을 목적함수로 사용하였다. 다목적 유전자알고리즘 최적화과정을 통하여 퍼지관리제어기의 파레토 최적해집합을 효과적으로 얻을 수 있었다. 다목적 유전자알고리즘에 의하여 개발된 퍼지관리제어기는 가중합방법의 제어기보다 매우 우수한 성능을 나타내었다.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.783-783
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• 2012

본 연구에서는 낙동강 임하댐 유역을 대상으로 엔트로피 이론(혼합분포 적용)과 관측소의 공간적 분포를 동시에 고려하여 강우관측망을 평가하였다. 일반적으로 혼합분포를 이용하는 강우관측망 평가는 연속분포를 이용하는 경우 비해 강우의 시공간적 간헐성을 고려할 수 있다는 장점이 있다. 아울러 유역의 면적평균강우량을 산정시 강우관측소는 균등하게 설치된 경우가 가장 이상적이며, 이를 최근린 지수(Nearest neighbor index)를 이용하여 강우관측소 간에 공간적 분포를 등급화하였다. 최근린 지수는 임의의 점에 가장 가까운 인접 점들 간의 거리 특성을 이용하는 방법으로 점의 분포를 보다 지리적으로 파악할 수 있다. 본 연구에서는 엔트로피의 최대 정보전달량 및 강우관측소의 등급을 동시에 고려하기 위해 유클리디언 거리를 이용하여 2개의 목적함수를 통합하였으며, 이를 MOGA(Multi Objective Genetic Algorithm)를 이용하여 최적관측망을 선정하였다. 그 결과 MOGA를 이용하여 관측망을 평가한 경우 엔트로피 이론만을 적용했을 때보다 최적관측소가 보다 분산됨을 확인하였다.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.170-175
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• 2001

The design cycle associated with large engineering systems requires an initial decomposition of the complex system into design processes which are coupled through the transference of output data. Some of these design processes may be grouped into iterative subcycles. In analyzing or optimizing such a coupled system, it is essential to determine the best order of the processes within these subcycles to reduce design cycle time and cost. This is accomplished by decomposing large multidisciplinary problems into several multidisciplinary analysis subsystems (MDASS) and processing it in parallel. This paper proposes new strategy for parallel decomposition of multidisciplinary problems to improve design efficiency by using the multiple objective genetic algorithm (MOGA), and a sample test case is presented to show the effects of optimizing the sequence with MOGA.

• Journal of Korea Water Resources Association
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• v.40 no.9
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• pp.687-696
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• 2007

Preference ordering approach is applied to optimize the parameters of Tank model using multi-objective genetic algorithm (MOGA). As more than three multi-objective functions are used in MOGA, too many non-dominated optimal solutions would be obtained thus the stakeholder hardly find the best optimal solution. In order to overcome this shortcomings of MOGA, preference ordering method is employed. The number of multi-objective functions in this study is 4 and a single Pareto-optimal solution, which is 2nd order efficiency and 3 degrees preference ordering, is chosen as the most preferred optimal solution. The comparison results among those from Powell method and SGA (simple genetic algorithm), which are single-objective function optimization, and NSGA-II, multi-objective optimization, show that the result from NSGA-II could be reasonalby accepted since the performance of NSGA-II is not deteriorated even though it is applied to the verification period which is totally different from the calibration period for parameter estimation.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.409-416
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• 2009

This paper presents a tool called Quality-Cost optimization system (QCOS), which integrates Multi-Objective Genetic Algorithm (MOGA) and Quality Function Deployment (QFD), for tradeoff between quality and cost of the unitized metal curtain-wall unit. A construction owner as the external customer pursues to maximize the quality of the curtain-wall unit. However, the contractor as the internal customer pursues to minimize the cost involved in designing, manufacturing and installing the curtain-wall unit. It is crucial for project manager to find the tradeoff point which satisfies the conflicting interests pursued by the both parties. The system would be beneficial to establish a quality plan satisfying the both parties. Survey questionnaires were administered to the construction owner who has an experience of curtain-wall project, the architects who are the independent assessor, and the contractors who were involved in curtain-wall design and installation. The Customer Requirements (CRs) and their importance weights, the relationship between CRs and Technical Attributes (TAs) consisting of a curtain-wall unit, and the cost ratios of each components consisting curtain-wall unit are obtained from the three groups mentioned previously. The data obtained from the surveys were used as the QFD input to compute the Owner Satisfaction (OS) and Contractor Satisfaction (CS). MOGA is applied to optimize resource allocation under limited budget when multi-objectives, OS and CS, are pursued at the same time. The deterministic multi-objective optimization method using MOGA and QFD is extended to stochastic model to better deal with the uncertainties of QFD input and the variability of QFD output. A case study demonstrates the system and verifies the system conformance.

• Bulletin of the Society of Naval Architects of Korea
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• v.31 no.1
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• pp.34-38
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• 1994

본 연구에서는 Genetic Algorithm을 사용하여 상기의 문제를 해결하고자 한다. 특히 다목적 함수 최적화에는 한 번의 최적화 계산으로 Pareto최적해 집합이 동시에 구해지는 새로운 방법인 MOGA(Multicriteria Optimization by Genetic Algorithm)을 개발하였다. 먼저 Genetic Alorithm의 기본 특성에 대해 살펴보고, 다양한 종류의 문제를 통해 Genetic Algorithm의 유용 성을 검토하였다.