• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.5
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• pp.325-331
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• 2004

The multiobjective optimization (MO) problem usually includes the conflicting objectives and the use of conventional optimization algorithms for MO problem does not so good approach to obtain an effective optimal solution. In this paper, genetic algorithm (GA) as an effective method is used to solve such MO problem of brushless DC motor (BLDCM). 3D equivalent magnetic circuit network (EMCN) method which enables us to reduce the computational burden is also used to consider the 3D structure of BLDCM. In order to effectively obtain a set of Pareto optimal solutions in MO problem, ranking method proposed by Fonseca is applied. The objective functions are decrease of cogging torque and increase of torque respectively. The airgap length, teeth width and magnetization angle of PM are selected for the design variables. The experimental results are also shown to confirm the validity of the optimization results.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3291-3300
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• 2023

Recently molybdenum alloys have been introduced as accident tolerating materials for cladding of fuel rods. Molybdenum element has seven stable isotopes with different neutron absorption cross section used in various fields, including nuclear physics and radioisotope production. This study presents separation approaches for all intermediate isotopes of molybdenum element by squared-off cascades using a newly developed numerical code with Salp Swarm Algorithm (SSA) optimization algorithm. The parameters of cascade including feed flow rate, feed entry stage, cascade cut, input feed flow rate to gas centrifuges (GCs), and cut of the first stage are optimized to maximize both isotope recovery and cascade capacity. The squared off and squared cascades are studied, and the efficiencies are compared. The results obtained from the optimization showed that for the selected squared off cascade, Mo⁹⁴ in four separation steps, Mo⁹⁵ in five steps, Mo⁹⁶ in six steps, Mo⁹⁷ in seven steps, and Mo⁹⁸ in two steps are separated to the desired concentrations. The highest recovery factor is obtained 63% for Mo⁹⁴ separation and lowest recovery factor is found 45% for Mo⁹⁵.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.211-220
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• 2017

The application of the theoretical model to real assembly lines has been one of the biggest challenges for researchers and industrial engineers. There should be some realistic approach to achieve the conflicting objectives on real systems. Therefore, in this paper, a model is developed to synchronize a real system (A discrete event simulation model) with a theoretical model (An optimization model). This synchronization will enable the realistic optimization of systems. A job assignment model of the assembly line is formulated for the evaluation of proposed realistic optimization to achieve multiple conflicting objectives. The objectives, fluctuation in cycle time, throughput, labor cost, energy cost, teamwork and deviation in the skill level of operators have been modeled mathematically. To solve the formulated mathematical model, a multi-objective simulation integrated hybrid genetic algorithm (MO-SHGA) is proposed. In MO-SHGA each individual in each population acts as an input scenario of simulation. Also, it is very difficult to assign weights to the objective function in the traditional multi-objective GA because of pareto fronts. Therefore, we have proposed a probabilistic based linearization and multi-objective to single objective conversion method at population evolution phase. The performance of MO-SHGA is evaluated with the standard multi-objective genetic algorithm (MO-GA) with both deterministic and stochastic data settings. A case study of the goalkeeping gloves assembly line is also presented as a numerical example which is solved using MO-SHGA and MO-GA. The proposed research is useful for the development of synchronized human based assembly lines for real time monitoring, optimization, and control.

• Structural Engineering and Mechanics
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• v.77 no.3
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• pp.417-439
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• 2021

The geometric nonlinearity has been successfully integrated with the design of steel structural system. Thus, the tubular lattice girder, one application of steel structural systems have already been optimized to obtain an economic design following the completion of computationally expensive design procedure. In order to decrease its computing cost, this study proposes to employ five multi-objective metaheuristics for the design optimization of geometrically nonlinear tubular lattice girder. Then, the employed multi-objective optimization algorithms (MOAs), NSGAII, PESAII, SPEAII, AbYSS and MoCell are evaluated considering their computing performances. For an unbiased evaluation of their computing performance, a tubular lattice girder with varying size-shape-topology and a benchmark truss design with 17 members are not only optimized considering the geometrically nonlinear behavior, but three benchmark mathematical functions along with the four benchmark linear design problems are also included for the comparison purpose. The proposed experimental study is carried out by use of an intelligent optimization tool named JMetal v5.10. According to the quantitative results of employed quality indicators with respect to a statistical analysis test, MoCell is resulted with an achievement of showing better computing performance compared to other four MOAs. Consequently, MoCell is suggested as an optimization tool for the design of geometrically nonlinear tubular lattice girder than the other employed MOAs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.2889-2909
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• 2017

Wireless sensor networks (WSNs) have attracted lots of attention in recent years due to their potential for various applications. In this paper, we seek how to efficiently deploy relay nodes into traditional static WSNs with constrained locations, aiming to satisfy specific requirements of the industry, such as average energy consumption and average network reliability. This constrained relay node deployment problem (CRNDP) is known as NP-hard optimization problem in the literature. We consider addressing this multi-objective (MO) optimization problem with an improved Artificial Bee Colony (ABC) algorithm with a linear local search (MOABCLLS), which is an extension of an improved ABC and applies two strategies of MO optimization. In order to verify the effectiveness of the MOABCLLS, two versions of MO ABC, two additional standard genetic algorithms, NSGA-II and SPEA2, and two different MO trajectory algorithms are included for comparison. We employ these metaheuristics on a test data set obtained from the literature. For an in-depth analysis of the behavior of the MOABCLLS compared to traditional methodologies, a statistical procedure is utilized to analyze the results. After studying the results, it is concluded that constrained relay node deployment using the MOABCLLS outperforms the performance of the other algorithms, based on two MO quality metrics: hypervolume and coverage of two sets.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.26 no.9
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• pp.701-706
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• 2013

We have focused on the conversion efficiency of CIGS thin film solar cell prepared by co-evaporation method as well as the optimization of process condition. The total thickness of back electrode was fixed at 1 ${\mu}m$ and the structural, electric and optical properties of CIGS thin film were investigated by varying the thickness of Mo:Na bottom layer from 0 to 500 nm. From the experimental results, the content of Na was appeared as 0.28 atomic percent when the thickness of Mo:Na layer was 300 nm with compactly densified plate-shape surface morphology. From the XRD measurements, (112) plane was the strongest preferential orientation together with secondary (220) and (204) planes affecting to the crystallization. The lowest roughness and resistivity were 2.67 nm and 3.9 ${\Omega}{\cdot}cm$, respectively. In addition, very high carrier density and hole mobility were recorded. From the optimization of Mo:Na layer, we have achieved the conversion efficiency of 9.59 percent.

• Journal of the Korean Chemical Society
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• v.25 no.3
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• pp.145-151
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• 1981

The intrinsic reactivity of $S_N2$reaction in the gas phase was discussed MO theoretically (CNDO/2). We investigated the changes in geometry and electronic structure by means of the partial geometry optimization for reactantes, transition states, and products with various nucleophiles and leaving groups. We found that it was possible to discuss qualitatively the reactivity of $S_N2$ reaction with CNDO/2 MO calculation and the reactivity was controlled by basicity and of induced polarizability.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2001.05a
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• pp.301-306
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• 2001

In this paper, friction welding optimization for 1Cr0.5Mo to STS304 and AE applications for the weld quality evaluation were investigated The important results of this study are as follows : 1. The techiques for dissimilar friction welding optimization if the elevated temperature materials 1Cr0.5Mo and STS304 and its real-time weld quality evaluation by AE were developed, considering on both strength and toughness. 2. Quantitative relationship was identified among welding condition, weld quality and cumulative AE counts.

• Proceeding of EDISON Challenge
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• 2014.03a
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• pp.511-513
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• 2014

$MoS_2$ monolayer에 Au 원자를 흡착시켰을 때 가장 안정한 위치를 찾아 내기위한 연구를 수행하였다. 이를 위하여 $MoS_2$ $1{\times}1$ unit cell 위의 on-top, bridge, hollow 위치에 Au 원자를 놓고 DFT 기반 제일원리 계산을 통하여 최적화된 구조에서의 에너지를 계산, 비교하였다. 그 결과 S 원자 위에 Au 원자가 흡착 되었을 때 가장 안정한 구조를 이루는 것을 알 수 있었다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.1
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• pp.135-142
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• 2012

In this paper, we proposed a new architecture called radial basis function-based polynomial neural networks classifier that consists of heterogeneous neural networks such as radial basis function neural networks and polynomial neural networks. The underlying architecture of the proposed model equals to polynomial neural networks(PNNs) while polynomial neurons in PNNs are composed of Fuzzy-c means-based radial basis function neural networks(FCM-based RBFNNs) instead of the conventional polynomial function. We consider PNNs to find the optimal local models and use RBFNNs to cover the high dimensionality problems. Also, in the hidden layer of RBFNNs, FCM algorithm is used to produce some clusters based on the similarity of given dataset. The proposed model depends on some parameters such as the number of input variables in PNNs, the number of clusters and fuzzification coefficient in FCM and polynomial type in RBFNNs. A multiobjective particle swarm optimization using crowding distance (MoPSO-CD) is exploited in order to carry out both structural and parametric optimization of the proposed networks. MoPSO is introduced for not only the performance of model but also complexity and interpretability. The usefulness of the proposed model as a classifier is evaluated with the aid of some benchmark datasets such as iris and liver.