• Journal of Korea Water Resources Association
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• v.30 no.4
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• pp.347-355
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• 1997

In this study, the applicability of genetic algorithms into the parameter estimation of storage function method for flood routing model is investigated. Genetic algorithm is mathematically established theory based on the process of Darwinian natural selection and survival of fittest. It can be represented as a kind of search algorithms for optima point in solution space and make a reach on optimal solutions through performance improvement of assumed model by applying the natural selection of life as mechanical learning province. Flood events recorded in the Daechung dam are selected and used for the parameter estimation and verification of the proposed parameter estimation method by the split sample method. The results are analyzed that the performance of the model are improved including peak discharge and time to peak and shown that the parameter Rsa, and f1 are most sensitive to storage function model. Based on the analysis for estimated parameters and the comparison with the results from experimental equations, the applicability of genetic algorithm is verified and the improvements of those equations will be used for the augmentation of flood control efficiency.

• Journal of KIISE:Software and Applications
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• v.30 no.10
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• pp.912-924
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• 2003

Design restructuring improves software quality by reorganizing design elements and reduces maintenance cost. Object-oriented metrics can help to detect design flaws and find transformations to reorganize design elements. Basically, the transformations must preserve the behavior of an initial system. This paper describes a metric based restructuring technique preserving the behavior of object-oriented designs, founded on set theory, and gives its validity by applying the technique to applications written in Java. This paper also compares the technique with a technique using simulated annealing algorithm to show its effectiveness.

• Structural Engineering and Mechanics
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• v.47 no.5
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• pp.713-727
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• 2013

In current study, natural frequency response of fiber metal laminated (FML) fibrous composite panels is optimized under different combination of the three classical boundary conditions using particle swarm optimization (PSO) algorithm and finite strip method (FSM). The ply angles, numbers of layers, panel length/width ratios, edge conditions and thickness of metal sheets are chosen as design variables. The formulation of the panel is based on the classical laminated plate theory (CLPT), and numerical results are obtained by the semi-analytical finite strip method. The superiority of the PSO algorithm is demonstrated by comparing with the simple genetic algorithm.

• 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.

• Journal of electromagnetic engineering and science
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• v.18 no.3
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• pp.145-153
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• 2018

In this paper, the efficiency of single-input multiple-output (SIMO) wireless power transfer systems is examined. Closed-form solutions for the receiver loads that maximize either the total efficiency or the efficiency for a specific receiver are derived. They are validated with the solutions obtained using genetic algorithm (GA) optimization. The optimum load values required to maximize the total efficiency are found to be identical for all the receivers. Alternatively, the loads of receivers can be adjusted to deliver power selectively to a receiver of interest. The total efficiency is not significantly affected by this selective power distribution. A SIMO system is fabricated and tested; the measured efficiency matches closely with the efficiency obtained from the theory.

• Industrial Engineering and Management Systems
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• v.12 no.1
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• pp.36-40
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• 2013

The paper discusses the problem of how to allocate the fund to a large number of individuals in a higher school so as to bring a higher utility return based on the theory of uncertain set. Suppose that experts can assign each invested individual a corresponding nondecreasing membership function on a close interval I according to its actual level and developmental foreground. The membership degree at the fund $x{\in}I$ is called utility degree from fund x, and product (minimum) of utility degrees of distributed funds for all invested individuals is called united utility degree from the fund. Based on the above concepts, we present an uncertain optimization model, called Maximal United Utility Degree (or Maximal Membership Degree) model for fund distribution. Furthermore, we use nondecreasing polygonal functions defined on close intervals to structure a mathematical maximal united utility degree model. Finally, we design a genetic algorithm to solve these models.

• Journal of KIISE:Computer Systems and Theory
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• v.26 no.6
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• pp.729-741
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• 1999

WWW의 사용량이 대단히 빠르게 늘어남에 따라 웹 프락시 서버의 캐싱 기능은 그 중요성이 날로 증가하고 있다. 본 논문에서는 이러한 웹 프락시 캐시의 교체 정책을 웹 요구의 특성과 프락시 서버환경자체를 고려하여 주어진 환경에 맞는 교체 정책이 되도록 유전 알고리즘을 사용하여 설계하는 방법을 제시하였다. 이 방법은 대부분의 기존 연구에서처럼 특정한 교체 정책 자체를 제시하는 것이 아니라, 주어진 프락시 환경에 적합한 교체 정책을 동적으로 설계할 수 있는 방법을 제시한다는 점메서 기존의 연구보다 일반성이 높다.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.521-523
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• 1999

In this paper, we propose the hybrid fuzzy controller(HFC) and the hybrid auto-tuning algorithm. The proposed HFC combined a PID controller with a fuzzy controller concurrently produces the better output performance such as sensitivity improvement in steady state and robustness in transient state than any other controller. In addition, a hybrid auto-tuning algorithm which consists of genetic algorithm and complex algorithm to automatically generate weighting factor, scaling factors and PID control gains optimizes the output of HFC. As an typical example of non-linear system in control theory an inverted pendulum will be controlled by the suggested HFC and illustrated the performance and applicability of this proposed method by simulation.

• Annual Conference of KIPS
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• 2003.05b
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• pp.1063-1066
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• 2003

지능형 게임 개발을 위하여 게임 이론의 정의, 게임의 구성요소, 전략적 게임의 분석을 통해 게임에 대한 배경 환경을 살펴보고, 보다 사실적 느낌 전달을 위한 게임 애니메이션과 게임에 적용되는 인공지능 기술을 퍼지 이론, 뉴럴네트웍으로 분류하여 적용 현황을 살펴보았다. 즉 게임처럼 수학적 표현이 어려운 경우 해결점을 퍼지 이론에서, 캐릭터의 움직임을 제어하는 퍼지 Rule Base를 찾아내는 연구를 신경망 인공지능을 통해 해결하는 과정을 살펴보고 국부해의 단점을 갖는 신경망 인공지능의 불투명성 해결 방법을 유전자 알고리즘에서 찾았다. 결론적으로 게임에서 이루어지는 물리적 특성인 충돌에 대한 충돌검사 알고리즘, 충돌반응에 대한 최적화를 유전자 알고리즘을 적용하여 해결하였다.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.706-708
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• 1999

In this study, a seesaw system is used as an example to demonstrate effectiveness of the control theory. This problem which keeps a seesaw's balance is corresponded to one of regulating problems. In this regard, a controller is designed by LQ techniques and an observer is implemented and applied to estimate states which cannot be actually measured in this system. And the genetic algorithm is utilized to systematically choose weighting factors in the given cost function. The performance of the controller is verified through the experiment results.