• Journal of Internet Computing and Services
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• v.22 no.1
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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.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.51 no.1
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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.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.8
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• pp.746-752
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• 2009

In this paper, we deal with the development of an optimal design program for a triple-band PTFA(Planar Inverted-F Antenna) of 433 MHz, 912 MHz and 2.45 GHz by using evolution strategy. Generally, the resonance frequency of the PIFA is determined by the width and length of a U-type slot used. However the resonance frequencies of the multiple U slots are varied by the mutual effect of the slots. Thus the optimal width and length of U-type slots are determined by using an optimal design program based on the evolution strategy. To achieve this, an interface program between a commercial EM analysis tool and the optimal design program is constructed for implementing the evolution strategy technique that seeks a global optimum of the objective function through the iterative design process consisting of variation and reproduction. The resonance frequencies of the triple-band PIFA yielded by the optimal design program are 430 MHz, 910.5 MHz and 2.458 GHz that show a good agreement to the design target values.

• 한국생물공학회:학술대회논문집
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• 2001.11a
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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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• v.19 no.3
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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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.665-666
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• 2022

Differential evolution is an efficient algorithm for continuous optimization problems. However, applying differential evolution to solve large-scale optimization problems quickly degrades performance and exponentially increases runtime. To overcome this problem, a new cooperative coevolution differential evolution based on Spark (referred to as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.559-560
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• 2021

Differential evolution is an efficient algorithm for solving continuous optimization problems. However, applying differential evolution to solve large-scale optimization problems dramatically degrades performance and exponentially increases runtime. Therefore, a novel cooperative coevolution differential evolution based on Spark (known as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC.

• Proceedings of the KIEE Conference
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• 2003.10b
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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.

• Proceedings of the KIEE Conference
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• 2009.07a
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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.

• Proceedings of the KIEE Conference
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• 2006.10c
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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.