• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.753-755
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• 2005

The Black-Scholes (BS) option pricing model is a landmark in contingent claim theory and has found wide acceptance in financial markets. However, it has a difficulty in the use of the model, because the volatility which is a nonlinear function of the other parameters must be estimated. The more accurately investors are able to estimate this value, the more accurate their estimates of theoretical option values will be. This paper proposes a new model which is based on Particle Swarm Optimization (PSO) for finding more precise theoretical values of options in the field of evolutionary computation (EC) than genetic algorithm (GA)or calculus-based search techniques to find estimates of the implied volatility.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.322-324
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• 2005

In this paper, we introduce the advanced Self-Organizing Fuzzy Polynomial Neural Network based on optimized FPN by evolutionary algorithm and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed model gives rise to a structurally and parametrically optimized network through an optimal parameters design available within Fuzzy Polynomial Neuron(FPN) by means of GA. Through the consecutive process of such structural and parametric optimization, an optimized and flexible the proposed model is generated in a dynamic fashion. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

• 한국공간정보시스템학회:학술대회논문집
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• 2004.05a
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• pp.43-52
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• 2004

The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithms. The algorithm can perform both shape and topology optimum designs of trusses. The developed algerian was implemented in a computer program. For the optimum design, the objective function is the weight of trusses and the constraints are stress and displacement. The basic search method for the optimum design is the genetic algorithms. The algorithm is known to be very efficient for the discrete optimization. The genetic algorithm consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. The efficiency and validity of the developed size, shape and topology discrete optimum design algorithms were verified by applying the algorithm to optimum design examples

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.494-496
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• 1995

This paper presents a methodology of improving a conventional numerical model in power systems using GAs and a new GAs-based model which can directly solve the real-valued optimum in the optimization procedure. The power flow which is well known to the power engineer is solved using the proposed GAs as an alternative way of the traditional optimization method. In applying GAs to the power flow, both the notions on a way of the genetic representations and a realization of the genetic operators are fully discussed to evaluate the GA's effectiveness.

• Journal of the Korea Society for Simulation
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• v.6 no.1
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• pp.71-84
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• 1997

For many optimization problems where some of the system components are stochastic, the objective functions cannot be represented analytically. Therefore, modeling by computer simulation is one of the most effective means of studying such complex systems. Many, if not most, simulation optimization problems have multiple aspects. Historically, multiple aspects have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic algorithm (GA), however, is readily modified to deal with multiple aspects. In this paper we propose a MAGA (Multiple Aspects-based Genetic Algorithm) as an algorithm for finding the Pareto optimal set. We demonstrate its ability to find and maintain a diverse "Pareto optimal population" on two problems.

• Transactions on Electrical and Electronic Materials
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• v.14 no.1
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• pp.32-35
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• 2013

In this paper, a manufacturing process was developed for fabricating high-quality AlGaN/GaN high electron mobility transistors (HEMTs) on silicon carbide (SiC) substrates. Various conditions and processing methods regarding the ohmic contact and pre-metal-deposition $BCl_3$ etching processes were evaluated in terms of the device performance. In order to obtain a good ohmic contact performance, we tested a Ti/Al/Ta/Au ohmic contact metallization scheme under different rapid thermal annealing (RTA) temperature and time. A $BCl_3$-based reactive-ion etching (RIE) method was performed before the ohmic metallization, since this approach was shown to produce a better ohmic contact compared to the as-fabricated HEMTs. A HEMT with a 0.5 ${\mu}m$ gate length was fabricated using this novel manufacturing process, which exhibits a maximum drain current density of 720 mA/mm and a peak transconductance of 235 mS/mm. The X-band output power density was 6.4 W/mm with a 53% power added efficiency (PAE).

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.423-434
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• 2004

In this paper, we introduce a new topology of Self-Organizing Polynomial Neural Networks (SOPNN) based on genetically optimized Multi-Layer Perceptron (MLP) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. Let us recall that the design of the 'conventional' SOPNN uses the extended Group Method of Data Handling (GMDH) technique to exploit polynomials as well as to consider a fixed number of input nodes at polynomial neurons (or nodes) located in each layer. However, this design process does not guarantee that the conventional SOPNN generated through learning results in optimal network architecture. The design procedure applied in the construction of each layer of the SOPNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomials, and input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between the approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented using pH neutralization process data as well as sewage treatment process data. A comparative analysis indicates that the proposed SOPNN is the model having higher accuracy as well as more superb predictive capability than other intelligent models presented previously.reviously.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.1
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• pp.1-9
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• 2019

In electric agricultural machine the gearbox is used to increase torque and lower the output speed of the motor shaft. The gearbox consists of several shafts, helical gears and spur gears works in series. Optimization plays an important role in gear design as reducing the weight or volume of a gear set will increase its service life and improve the bearing capacity. In this paper the basic design parameters for gear like shaft diameter and face width are considered as the input variables. The bending stress and material volume is considered as the objective function. ANSYS was used to investigate the bending stress when the variable was changed. Artificial Neural Network (ANN) was used to obtain the mathematical model of the system based on the bending stress behaviour. The ANN was used since the output system is nonlinear. The Genetic Algorithm (GA)　technique of optimization is used to obtain the optimized values of shaft diameter and face width on the pinion based on the ANN mathematical model and the results are compared as that obtained using the traditional method. The ANN and GA were performed using MATLAB. The simulation results were shown that the proposed algorithm was successfully calculated the value of shaft diameter and face width to obtain the minimal bending stress and material volume of the gearbox.

• Journal of the Korea Society for Simulation
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• v.22 no.2
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• pp.33-40
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• 2013

This paper explores a border security system based on agent-based modeling and simulation (ABMS). The ABMS software platform, map aware non-uniform automata, is used to model various scenarios and evaluate the border security system given a set of infiltrators who have evolutionary behavior governed by genetic algorithm (GA). we formulated an optimization model and approximately solved it using a GA in order to capture near optimal behavior of an infiltrating force. The results presented give two significant insights for our border security system in that optimizing the infiltrator's behavior can make a significant difference and the quantitative results regarding the infiltrator's avoidance of each asset can be viewed as capturing their relative importance.

• Journal of Biosystems Engineering
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• v.38 no.2
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• pp.149-162
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• 2013

Purpose: In image segmentation via thresholding, Otsu and Kapur methods have been widely used because of their effectiveness and robustness. However, computational complexity of these methods grows exponentially as the number of thresholds increases due to the exhaustive search characteristics. Methods: Particle swarm optimization (PSO) and genetic algorithms (GAs) can accelerate the computation. Both methods, however, also have some drawbacks including slow convergence and ease of being trapped in a local optimum instead of a global optimum. To overcome these difficulties, we proposed two new multi-level thresholding methods based on Bacteria Foraging PSO (BFPSO) and real-coded GA algorithms for fast segmentation. Results: The results from BFPSO and real-coded GA methods were compared with each other and also compared with the results obtained from the Otsu and Kapur methods. Conclusions: The proposed methods were computationally efficient and showed the excellent accuracy and stability. Results of the proposed methods were demonstrated using four real images.