• Structural Engineering and Mechanics
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• v.43 no.6
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• pp.795-821
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• 2012

The size and topology of geometrically nonlinear dome structures are optimized thereby minimizing both its entire weight & joint (node) displacements and maximizing load-carrying capacity. Design constraints are implemented from provisions of American Petroleum Institute specification (API RP2A-LRFD). In accordance with the proposed design constraints, the member responses computed by use of arc-length technique as a nonlinear structural analysis method are checked at each load increment. Thus, a penalization process utilized for inclusion of unfeasible designations to genetic search is correspondingly neglected. In order to solve this complex design optimization problem with multiple objective functions, Non-dominated Sorting Genetic Algorithm II (NSGA II) approach is employed as a multi-objective optimization tool. Furthermore, the flexibility of proposed optimization is enhanced thereby integrating an automatic dome generating tool. Thus, it is possible to generate three distinct sphere-shaped dome configurations with varying topologies. It is demonstrated that the inclusion of brace (diagonal) members into the geometrical configuration of dome structure provides a weight-saving dome designation with higher load-carrying capacity. The proposed optimization approach is recommended for the design optimization of geometrically nonlinear dome structures.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.399-408
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• 2001

This paper presents a simultaneous optimization approach of case-based reasoning (CBR) using a genetic algorithm(GA) for the prediction of stock price index. Prior research suggested many hybrid models of CBR and the GA for selecting a relevant feature subset or optimizing feature weights. Most studies, however, used the GA for improving only a part of architectural factors for the CBR system. However, the performance of CBR may be enhanced when these factors are simultaneously considered. In this study, the GA simultaneously optimizes multiple factors of the CBR system. Experimental results show that a GA approach to simultaneous optimization of CBR outperforms other conventional approaches for the prediction of stock price index.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.9
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• pp.548-556
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• 2014

To cope with recently increasing demand for data traffics, heterogeneous networks have been actively studied, where small cells are deployed within a macro cell coverage with the same frequency band. To mitigate the interference from the macro cell to small cells, an enhanced Inter-cell Interference Coordination (eICIC) technique has been proposed, where ABS (Almost Blank Subframe) is used in time domain. However, there is a waste of resource since no data is transmitted in a macro-cell in ABS. In this paper, we propose a new interference management method by using a 3D sector beam based on Active Antenna System (AAS), where Genetic Algorithm (GA) is applied to reduce the antenna gain toward a small-cell. With the proposed scheme, the macro-cell and small cells can transmit data at the same time with the AAS antenna pattern generating reduced interference to small cells. The performance of the proposed scheme is evaluated by using an LTE-Advanced system level simulator.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.215-222
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• 2001

Neural network based models of semiconductor manufacturing processes have been shown to offer advantages in both accuracy and generalization over traditional methods. However, model development is often complicated by the fact that back-propagation neural networks contain several adjustable parameters whose optimal values unknown during training. These include learning rate, momentum, training tolerance, and the number of hidden layer neurOnS. This paper presents an investigation of the use of genetic algorithms (GAs) to determine the optimal neural network parameters for the modeling of plasma-enhanced chemical vapor deposition (PECVD) of silicon dioxide films. To find an optimal parameter set for the neural network PECVD models, a performance index was defined and used in the GA objective function. This index was designed to account for network prediction error as well as training error, with a higher emphasis on reducing prediction error. The results of the genetic search were compared with the results of a similar search using the simplex algorithm.

• Steel and Composite Structures
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• v.32 no.4
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• pp.455-466
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• 2019

Despite the rapid advancement in computing resources, many real-life design and optimization problems in structural engineering involve huge computation costs. To counter such challenges, approximate models are often used as surrogates for the highly accurate but time intensive finite element models. In this paper, surrogates for first-order shear deformation based finite element models are built using a polynomial regression approach. Using statistical techniques like Box-Cox transformation and ANOVA, the effectiveness of the surrogates is enhanced. The accuracy of the surrogate models is evaluated using statistical metrics like $R^2$, $R^2{_{adj}}$, $R^2{_{pred}}$ and $Q^2{_{F3}}$. By combining these surrogates with nature-inspired multi-criteria decision-making algorithms, namely multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO), the optimal combination of various design variables to simultaneously maximize fundamental frequency and frequency separation is predicted. It is seen that the proposed approach is simple, effective and good at inexpensively producing a host of optimal solutions.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.145-152
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• 2021

In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancement coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. To improve the convergence, distribution, and diversity of the classic, rapidly non-dominated genetic trial algorithm, to solve limited high-dimensional multi-objective problems, we propose a decomposition-based restricted genetic algorithm for dominance (DBCDP-NSGA-II).

• Transactions of the Korean hydrogen and new energy society
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• v.32 no.4
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• pp.212-218
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• 2021

In this study, we conducted a design optimization of the Type 4 composite pressure vessels to enhance the pressure-resistant performance of the vessels while keeping the thickness of the composite layer. The design variables for the optimization were the stacking angles of the helical layers of the vessels to improve the performance. Since the carbon fibers are expensive material, it is desirable to reduce the use of the carbon fibers by applying an optimal design of the composite pressure vessel. The structural analysis and optimization process for the design of Type 4 composite pressure vessels were carried out using a commercial finite element analysis software, Abaqus and a plug-in for automated simulation, Isight, respectively. The optimization results confirmed the performance and safety of the optimized Type 4 composite pressure vessels was enhanced by 12.84% compared to the initial design.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.1_2
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• pp.59-66
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• 2004

In this paper, a fast 3D model extraction algorithm with an enhanced PBIL of preserving depth consistency is proposed for the extraction of 3D depth information from 2D images. Evolutionary computation algorithms are efficient search methods based on natural selection and population genetics. 2D disparity maps acquired by conventional matching algorithms do not match well with the original image profile in disparity edge regions because of the loss of fine and precise information in the regions. Therefore, in order to decrease the imprecision of disparity values and increase the quality of matching, a compact genetic algorithm is adapted for matching environments, and the adaptive window, which is controlled by the complexity of neighbor disparities in an abrupt disparity point is used. As the result, the proposed algorithm showed more correct and precise disparities were obtained than those by conventional matching methods with relaxation scheme.

• Journal of Industrial Technology
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• v.29 no.B
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• pp.9-15
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• 2009

In the case of traditional binary encoding technique, it takes long time to converge the optimal solutions and brings about complexity of the systems due to encoding and decoding procedures. However, the ROGAs (real-coded genetic algorithms) do not require these procedures, and the k-means clustering algorithm can avoid global searching space. Thus, this paper proposes a new approach by using their advantages. The proposed method constructs the multiple predictors using the optimal differences that can reveal the patterns better and properties concealed in non-stationary time series where the k-means clustering algorithm is used for data classification to each predictor, then selects the best predictor. After selecting the best predictor, the cluster centers of the predictor are tuned finely via RCGKA in secondary tuning procedure. Therefore, performance of the predictor can be more enhanced. Finally, we verifies the prediction performance of the proposed system via simulating typical time series examples.

• Journal of Information Technology Applications and Management
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• v.16 no.1
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• pp.97-113
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• 2009

Collaborative filtering(CF) has been a very successful approach for building recommender system, but its widespread use has exposed to some well-known problems including sparsity and scalability problems. In order to mitigate these problems, we propose two novel models for improving the typical CF algorithm, whose names are ISCF(Item-Selected CF) and USCF(User-Selected CF). The modified models of the conventional CF method that condense the original dataset by reducing a dimension of items or users in the user-item matrix may improve the prediction accuracy as well as the efficiency of the conventional CF algorithm. As a tool to optimize the reduction of a user-item matrix, our study proposes genetic algorithms. We believe that our approach may relieve the sparsity and scalability problems. To validate the applicability of ISCF and USCF, we applied them to the MovieLens dataset. Experimental results showed that both the efficiency and the accuracy were enhanced in our proposed models.