• Journal of Electrical Engineering and Technology
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• v.4 no.3
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• pp.323-329
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• 2009

This paper presents a new approach for thermal unit commitment (UC) using a differential evolution (DE) algorithm. DE is an effective, robust, and simple global optimization algorithm which only has a few control parameters and has been successfully applied to a wide range of optimization problems. However, the standard DE cannot be applied to binary optimization problems such as UC problems since it is restricted to continuous-valued spaces. This paper proposes binary differential evolution (BDE), which enables the DE to operate in binary spaces and applies the proposed BDE to UC problems. Furthermore, this paper includes heuristic-based constraint treatment techniques to deal with the minimum up/down time and spinning reserve constraints in UC problems. Since excessive spinning reserves can incur high operation costs, the unit de-commitment strategy is also introduced to improve the solution quality. To demonstrate the performance of the proposed BDE, it is applied to largescale power systems of up to 100-units with a 24-hour demand horizon.

• 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.

• Journal of Communications and Networks
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• v.16 no.5
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• pp.523-533
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• 2014

Orthogonal frequency division multiple access (OFDMA) is a promising technique, which can provide high downlink capacity for the future wireless systems. The total capacity of OFDMA can be maximized by adaptively assigning subchannels to the user with the best gain for that subchannel, with power subsequently distributed by water-filling. In this paper, we propose the use of composite differential evolution (CoDE) algorithm to allocate the subchannels. The CoDE algorithm is population-based where a set of potential solutions evolves to approach a near-optimal solution for the problem under study. CoDE uses three trial vector generation strategies and three control parameter settings. It randomly combines them to generate trial vectors. In CoDE, three trial vectors are generated for each target vector unlike other differential evolution (DE) techniques where only a single trial vector is generated. Then the best one enters the next generation if it is better than its target vector. It is shown that the proposed method obtains higher sum capacities as compared to that obtained by previous works, with comparable computational complexity.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.549-557
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• 2014

Recently, analysis of bargaining game using evolutionary computation is essential issues in field of game theory. In this paper, we observe a bargaining game using co-evolution between two heterogenous artificial agents. In oder to model two artificial agents, we use a particle swarm optimization and a differential evolution. We investigate algorithm parameters for the best performance and observe that which strategy is better in the bargaining game under the co-evolution between two heterogenous artificial agents. Experimental simulation results show that particle swarm optimization outperforms differential evolution in the bargaining game.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.223-226
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• 1997

Usually, the Evolutionary Algorithms(EAs) are considered more efficient for optimal, system design because EAs can provide higher opportunity for obtaining the global optimal solution. This paper presents a mechanism of co-evolution consists of the two genetic algorithms(GAs). This mechanism includes host populations and parasite populations. These two populations are closely related to each other, and the parasite populations plays an important role of searching for useful schema in host populations. Host population represented by feedforward neural network and the result of co-evolution we will find the optimal structure of the neural network. We used the genetic algorithm that search the structure of the feedforward neural network, and evolution strategies which train the weight of neuron, and optimize the net structure. The validity and effectiveness of the proposed method is exemplified on the stabilization and position control of the inverted-pendulum system.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.399-404
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• 2012

Evolutionary algorithms such as genetic algorithm (GA) have been proven their effectiveness when applying to the design of fuzzy models. However, it tends to suffer from computationally expensWive due to the slow convergence speed. In this study, we propose an approach to develop fuzzy models by means of an improved differential evolution (IDE) to overcome this limitation. The improved differential evolution (IDE) is realized by means of an orthogonal approach and differential evolution. With the invoking orthogonal method, the IDE can search the solution space more efficiently. In the design of fuzzy models, we concern two mechanisms, namely structure identification and parameter estimation. The structure identification is supported by the IDE and C-Means while the parameter estimation is realized via IDE and a standard least square error method. Experimental studies demonstrate that the proposed model leads to improved performance. The proposed model is also contrasted with the quality of some fuzzy models already reported in the literature.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.165.1-165
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• 2001

The evolution strategy is a good evolutionary algorithm to find the global optimum of a real-valued function. Since many engineering problems can be formulated as real valued function optimization, the evolution strategy is frequently employed in engineering fields. However, in many engineering optimization problems, an optimization parameter is often restricted in the bounded region between two specified values, the minimum and the maximum limit, respectively. Since an offspring individual is generated randomly around a parent individual during mutation process of the evolution strategy, an individual outside the search region can be generated even if the parent is inside the search region. This paper proposes two new boundary-handling techniques for evolution strategies. One is the ...

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.8
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• pp.56-64
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• 1997

In distributed autonomous robotic systems, each robot must behaves by itself according to the its states and environements, and if necessary, must cooperates with other orbots in order to carray out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, the new learning and evolution method based on reinforement learning having delayed reward ability and distributed genectic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. Reinforement learning having delayed reward is still useful even though when there is no immediate reward. And by distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the perfodrmance of evolution, selective crossover using the characteristic of reinforcement learning is adopted in this paper, we verify the effectiveness of the proposed method by applying it to cooperative search problem.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.655-657
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• 1995

Variable Structure Controller is well known to be a robust controller. Recently, Evolution Strategy is used as a effective search algorithm. In this paper, we propose a Variable Structure Controller combined with Evolution Strategy. Evolution Strategy is used to estimate the unknown parameters, the control gain and the thickness of saturation function boundary layer of Variable Structure Controller. From the experiment, we found the proposed Variable Structure Controller shows accurate tracking ability and robust performance in the BLOC motor position control system.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1110-1112
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• 1996

In this paper, we propose a new approach for robust auto-tuning of PID gains using Evolution Strategy. Evolution Strategy is searching algorithm which imitate the principles of natural evolution as a method to solve parameter optimization problem and easy to use without any other special mathematical theory. Through the simulation of the speed control of a series-connected de motor, our proposed method shows more improved performance by finding optimal parameters of PID controller than a classical Ziegler-Nichols method.