• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.30-35
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• 1996

Penalty technique perhaps is the most common technique used in the genetic algorithms for constrained optimization problems. In recent years, several techniques have been proposed in the area of evolutionary computation. However, there is no general guideline on designing penalty function and constructing an efficient penalty function is quite problem-dependent. The purpose of the paper is to give a tutorial survey of recent works on penalty techniques used in genetic algorithms and to give a better classification on exisitng works, which may be helpful for revealing the intrinsic relationship among them and for providing some hints for further studies on penalty techniques.

• Journal of Power Electronics
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• v.3 no.2
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• pp.69-80
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• 2003

Computer aided engineering (CAE) is a systematic approach to develop a better product/application with maximum possible options and minimum transition time. This paper presents a comprehensive feasibility analysis of various CAE techniques for evaluation and optimization of power electronic converters and systems. Different CAE methods for analysis, design, and performance improvement are classified. In addition, their advantages compared to the conventional workbench experimental methods are explained in detail and through examples.

• Journal of information and communication convergence engineering
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• v.8 no.4
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• pp.370-376
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• 2010

Ensemble learning is a method for improving the performance of classification and prediction algorithms. However, its performance can be degraded due to multicollinearity problem where multiple classifiers of an ensemble are highly correlated with. This paper proposes genetic algorithm-based optimization techniques of SVM ensemble to solve multicollinearity problem. Empirical results with bankruptcy prediction on Korea firms indicate that the proposed optimization techniques can improve the performance of SVM ensemble.

• Nonlinear Functional Analysis and Applications
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• v.26 no.3
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• pp.611-628
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• 2021

In this paper, we propose a new branch-reduction-and-bound algorithm to solve the nonlinear knapsack problems by using general discrete monotonic optimization techniques. The specific properties of the problem are exploited to increase the efficiency of the algorithm. Computational experiments of the algorithm on problems with up to 30 variables and 5 different constraints are reported.

• Korean Management Science Review
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• v.21 no.3
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• pp.23-38
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• 2004

Recently the usage of mobile devices which enable the accessibility to Internet has been dramatically increased. Most of the mobile services, however, so far tend to be simple such as infotainment service. In order to fully taking advantage of wireless network and corresponding technology, personalized web service based on user's context could be needed. Meanwhile, optimization techniques have been vitally incorporated for optimizing the development and administration of electronic commerce. However, applying context-aware optimization mechanism to personalized mobile services is still very few. Hence, the purpose of this paper is to propose a methodology to incorporate optimization techniques into personalization services. Multi agent-based web service approach is considered to realize the methodology. To show the feasibility of the methodology proposed in this paper, a prototype system, CAMA-myOPt(Context-Aware Multi-Agent system for my Optimization), was implemented and adopted in mobile comparative shopping.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1999.03b
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• pp.165-168
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• 1999

The coil design of induction heating systems and their optimization are of paramount importance for semi-solid processing(SSP) The authors of this paper present the coil design and optimization of a 60 Hz induction heating system for ALTHIX 86S (Al-6%_Si-3%Cu-0.3%Mg) alloy. An objective function on the basis of the optimization process for the coil design is proposed by introducing an optimization technique. Finally the results of the optimal coil design are also applied to the induction heating process to obtain a fine globular microstructure. The proposed new objective function based on the computational techniques would contribute to obtaining the thixoformed components with good mechanical properties and reducing lead time.

• Korean Journal of Computational Design and Engineering
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• v.19 no.3
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• pp.245-252
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• 2014

Engineering applications of global optimization techniques are recently abundant in the literature and it may be caused by both new methodologies arising and faster computers coming out. Many of the optimization techniques are based on natural or biological phenomena. This study put focus on enhancing the performace of Cuckoo Search (CS) among them since it has the least number of parameters to tune. The proposed enhancement can be achieved by applying surrogate-based optimization at every cycle of CS, which fortifies the exploitation capability of the original method. The enhanced algorithm has been applied several engineering design problems with constraints. The proposed method shows comparable or superior performance to the original method.

• YAKHAK HOEJI
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• v.18 no.1
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• pp.49-58
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• 1974

Tablet product design problem was structured as constrained optimization problem and subsequently solved by multiple regression analysis and Lagrangian method of optimization. Aluminum flufenamate was the drug chosen and microcrystalline cellulose nad starch were the binder and disintegrant, respectivley. The effect of the binder and disintegrant concentration on tablet hardness, friability, volume, in vitro release rate, and urinary excretion rate of drug in human subjects was recorded. Since a reasonably rapid release rate of drug is generally an important objective in the design of solid dosage form, optimization of this parameter was employed in studying the applicability of constrained optimization to a pharmaceutical product design problem. In addition to finding optimal sitivity analysis studies to such problems was also illustratd. It would appear that prediction of the in vivo t$_{50%}$ response from a knowledge of the incitro t$_{50%}$ response can be made fairly accurately for the tablet system used in this study.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1990.10a
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• pp.91-94
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• 1990

The object of this study is to develop computer programs with which ordinary engineers can analyse or design steel-concrete composite teams using optimization technique. Various design ana construction techniques which could maximize load carrying capacities and control concrete tension cracks effectively are studied and included in the programs. Analysis results show that proposed construction techniques can reduce steel weight by about 10%∼20% compared with ordinary composite beam. Concrete tensile stresses can also be controlled affectively by the suggested techniques.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.911-917
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• 2017

There are generally three folds when developing neural network classifiers. They are as follows: 1) discriminant function; 2) lots of parameters in the design of classifier; and 3) high dimensional training data. Along with this viewpoint, we propose space search optimized polynomial neural network classifier (PNNC) with the aid of data preprocessing technique and simultaneous tuning strategy, which is a balance optimization strategy used in the design of PNNC when running space search optimization. Unlike the conventional probabilistic neural network classifier, the proposed neural network classifier adopts two type of polynomials for developing discriminant functions. The overall optimization of PNNC is realized with the aid of so-called structure optimization and parameter optimization with the use of simultaneous tuning strategy. Space search optimization algorithm is considered as a optimize vehicle to help the implement both structure and parameter optimization in the construction of PNNC. Furthermore, principal component analysis and linear discriminate analysis are selected as the data preprocessing techniques for PNNC. Experimental results show that the proposed neural network classifier obtains better performance in comparison with some other well-known classifiers in terms of accuracy classification rate.