• 인터넷정보학회논문지
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• 제22권1호
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• pp.1-11
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• 2021

Estimation of distribution algorithm (EDA) is a popular stochastic metaheuristic algorithm. EDA has been widely utilized in various optimization problems. However, it has been shown that the diversity of the population gradually decreases during the iterations, which makes EDA easily lead to premature convergence. This article introduces a hybrid estimation of distribution algorithm (EDA) with differential evolution (DE) based on self-adaptive strategy, namely HEDADE-SA. Firstly, an alternative probability model is used in sampling to improve population diversity. Secondly, the proposed algorithm is combined with DE, and a self-adaptive strategy is adopted to improve the convergence speed of the algorithm. Finally, twenty-five benchmark problems are conducted to verify the performance of HEDADE-SA. Experimental results indicate that HEDADE-SA is a feasible and effective algorithm.

• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제51권1호
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• pp.5-14
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• 2002

In this paper, we analyze inductive interference in conductive material around 345 kV power transmission line, and evaluate the effects of mitigation wires. Finite element method (FEM) is used to numerically compute induced eddy currents as well as magnetic fields around powder transmission lines. In the analysis model, geometries and electrical properties of various elements such as power transmission line, buried pipe lines, overhead ground wire, and conducting earth are taken into accounts. The calculation shows that mitigation wire reduces fairly good amount of eddy currents in buried pipe line. To find the optimum magnetic field shielding location of mitigation wire, we applied evolution strategy algorithm, a kind of stochastic approach, to the analysis model. Finally, it was shown that we can find more effective shielding effects with optimum location of one mitigation wire than with arbitrary location of multi-mitigation wires around the buried pipe lines.

• 한국전자파학회논문지
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• 제20권8호
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• pp.746-752
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• 2009

본 논문에서는 진화 알고리즘을 이용하여 U형 슬롯을 갖는 433 MHz, 912 MHz 및 2.45 GHz의 삼중 대역 PIFA(Planar Inverted-F Antenna)를 설계할 수 있는 프로그램 구현에 대해 다룬다. 일반적으로 U형 슬롯의 넓이와 길이에 따라 개구형 공진 주파수를 결정할 수 있으나, 두 개의 U형 슬롯의 영향으로 공진 주파수가 변화하기 때문에 진화 알고리즘을 이용한 최적 설계 프로그램을 통하여 최적의 U형 슬롯의 넓이와 길이를 구하였다. 변이와 재생산의 반복 과정을 통해 설계 목표에 적합한 설계 변수 값을 찾는 진화 알고리즘을 자동화하기 위하여 상용 전자장 해석 프로그램과 최적 설계 프로그램간의 연동 인터페이스를 구축하였다. 구현한 최적 설계 프로그램을 통한 삼중 대역 PIFA 시뮬레이션 결과는 430 MHz, 910.5 MHz 및 2.458 GHz의 공진 주파수를 갖는다.

• 한국생물공학회:학술대회논문집
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• 한국생물공학회 2001년도 추계학술발표대회
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• pp.578-581
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• 2001

A treatability study was conducted using a hydrocarbon-contaminated soil for the oPtimization of bioremediation strategy best fit to a given set of contamination. The applicability of nutrients, biosurfactant, and oil-degrading microorganisms were examined by monitoring $CO_2$ evolution and oil degradation The addition of inorganic nutrients in the form of slow released fertilizer accelerated the initial rate of $CO_2$ evolution by a factor of 3. The application of oil-degrading microorganisms did not significantly increased $CO_2$ evolution or biodegradation efficiency. Application of a commercial biosurfactant was most effect in terms of the total $CO_2$ evolution and the oil degradation rate. The results indicate that $CO_2$ evolution measurement was found to be a simple and reliable countermeasure of crude oil hydrocarbon mineralization for the rapid determination of the best-fit bioremediation strategy.

• Journal of information and communication convergence engineering
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• 제19권3호
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• pp.155-160
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• 2021

Differential evolution is an efficient algorithm for solving continuous optimization problems. However, its performance deteriorates rapidly, and the runtime increases exponentially when differential evolution is applied for solving large-scale optimization problems. Hence, a novel cooperative coevolution differential evolution based on Spark (known as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC. First, the large-scale problem is decomposed into several low-dimensional subproblems using the random grouping strategy. Subsequently, each subproblem can be addressed in a parallel manner by exploiting the parallel computation capability of the resilient distributed datasets model in Spark. Finally, the optimal solution of the entire problem is obtained using the cooperation mechanism. The experimental results on 13 high-benchmark functions show that the new algorithm performs well in terms of speedup and scalability. The effectiveness and applicability of the proposed algorithm are verified.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.665-666
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• 2022

미분 진화는 연속 최적화 문제에 대한 효율적인 알고리즘이다. 그러나 대규모 최적화 문제를 해결하기 위해 미분 진화를 적용하면 성능이 빠르게 저하되고 런타임이 기하급수적으로 증가한다. 이 문제를 극복하기 위해 Spark(SparkDECC라고 함)를 기반으로 하는 새로운 협력 공진화 미분 진화를 제안한다. 분할 정복 전략은 SparkDECC에서 사용된다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.559-560
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• 2021

차등 진화는 연속 최적화 문제를 해결하기 위한 효율적인 알고리즘이다. 그러나 대규모 최적화 문제를 해결하기 위해 차등 진화를 적용하면 성능이 급격히 저하되고 런타임이 기하급수적으로 증가한다. 따라서 Spark(SparkDECC로 알려짐)를 기반으로 하는 새로운 협력 공진화 차동 진화가 제안된다. 분할 정복 전략은 SparkDECC에서 사용된다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 추계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.113-115
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• 2003

This paper presents the optimal design of a brushless DC motor(BLDC) keeping the average torque and cogging torque of the initial model while minimizing the volume of magnet pole by FEM and evolution strategy. Experimental tests are performed by the finite element method(FEM), and the random based evolution strategy is applied for the shape optimization. The optimal result shows a largely reduced volume of magnet pole.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.1561_1562
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• 2009

In this paper, designed triple-band antenna of PIFA(Planar Inverted-F Antenna) structure with U-slot. We designed optimal PIFA structure using Evolution Strategy(ES) about two U-slot parameters. We materialized API(Application Program Interface) about EM simulator and Excel using VB(Visual Basic). The result of ES for triple-band PIFA are resonant frequency of 430MHz, 910.5MHz, 2458.5MHz.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.266-268
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• 2006

In this paper, we design a Linear Quadratic Gaussian(LQG) controller for active suspensions. We can improve the inherent suspension problem, trade-off between the ride quality and the suspension travel by selecting appropriate weights in the LQ-objective function. Using an optimization-algorithm, Evolution Strategy(ES), we find the proper control gains for selected frequencies, which have major effects on the vibrations of the vehicle's state variables. The frequencies and proper control gains are used for the neural network data. During a vehicle running, the trained on-line neural network is activated and provides the proper gains for non-trained frequencies.