• Journal of Electrical Engineering and Technology
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• 제8권1호
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• pp.53-63
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• 2013

To guide the decision making of the expert engineer specialized in power system operation and control; the practical OPF solution should take in consideration the critical situation due to severe loading conditions and fault in power system. Differential Evolution (DE) is one of the best Evolutionary Algorithms (EA) to solve real valued optimization problems. This paper presents simple Differential Evolution (DE) Optimization algorithm to solving multi objective optimal power flow (OPF) in the power system with shunt FACTS devices considering voltage deviation, power losses, and power flow branch. The proposed approach is examined and tested on the standard IEEE-30Bus power system test with different objective functions at critical situations. In addition, the non smooth cost function due to the effect of valve point has been considered within the second practical network test (13 generating units). The simulation results are compared with those by the other recent techniques. From the different case studies, it is observed that the results demonstrate the potential of the proposed approach and show clearly its effectiveness to solve practical OPF under contingent operation states.

• Journal of Communications and Networks
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• 제16권5호
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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.

• 한국CDE학회논문집
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• 제19권4호
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• pp.410-422
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• 2014

Unlike other weapon systems, a naval vessel has unique characteristics in that the vessel itself is a naval unit. In limited space, compartments with various objectives and characteristics need to be arranged, so that vessel performance is maximized. This paper studied a compartment arrangement algorithm that considers activity relationships among compartments and survivability of a vessel. Based on the study, a compartment arrangement application is developed that can generate various layout alternatives swiftly. The application developed in this study aims at automating a two dimensional compartment layout problem. A combinatorial optimization is performed with the differential evolution algorithm to achieve the optimized layout.

• 한국지능시스템학회논문지
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• 제12권5호
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• pp.451-461
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• 2002

차분 진화는 다양한 형태의 목적함수를 최적화하는데 매우 효율적인 방법임이 입증되었다 차분 진화의 가장 큰 이점은 개념적 단순성과 사용의 용이성이다. 그러나 차분 진화의 수렴성이 제어 파라미터에 매우 민감한 단점이 있다. 본 논문은 새로운 교배용 벡터 생성법과 제어 파라미터의 적응 메커니즘을 결합한 적응성 있는 차분 진화를 제안한다. 이는 수렴성을 해치지 않으면서 차분 진화를 보다 강인하게 만들며 사용이 쉽도록 해준다. 12가지 최적화 문제에 대해 제안한 방법을 시험하였다. 적응성 있는 차분 진화의 응용 사례로써 이벤트 예측을 위한 교사 클러스터링 방법을 제안한다. 이 방법을 진화에 의한 이벤트 클러스터링이라 부르며 데이터 모델링 검증에 널리 사용되는 4 가지 사례에 대해 그 성능을 시험하였다.

• 한국지능시스템학회논문지
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• 제17권5호
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• pp.679-683
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• 2007

Vapnik의 통계적 학습이론은 분류, 회귀, 그리고 군집화를 위하여 SVM(support vector machine), SVR(support vector regression), 그리고 SVC(support vector clustering)의 3가지 학습 알고리즘을 포함한다. 이들 중에서 SVC는 가우시안 커널함수에 기반한 지지벡터를 이용하여 비교적 우수한 군집화 결과를 제공하고 있다. 하지만 SVM, SVR과 마찬가지로 SVC도 커널모수와 정규화상수에 대한 최적결정이 요구된다 하지만 대부분의 분석작업에서 사용자의 주관적 경험에 의존하거나 격자탐색과 같이 많은 컴퓨팅 시간을 요구하는 전략에 의존하고 있다. 본 논문에서는 SVC에서 사용되는 커널모수와 정규화상수의 효율적인 결정을 위하여 차분진화를 이용한 DESVC(differential evolution based SVC)를 제안한다 UCI Machine Learning repository의 학습데이터와 시뮬레이션 데이터 집합들을 이용한 실험을 통하여 기존의 기계학습 알고리즘과의 성능평가를 수행한다.

• 대한수학회보
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• 제34권2호
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• pp.233-245
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• 1997

In this paper, we study the asymptotic behavior of a random evolution. Some examples of random evolution can be found in Chapter 12 of [2].

• Advances in Computational Design
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• 제1권4호
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• pp.315-327
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• 2016

Damage detection and localisation in structures is essential since it can be a means for preventive maintenance of those structures under service conditions. The use of structural modal data for detecting the damage is one of the most efficient methods. This paper presents comparative performance of various state-of-the-art meta-heuristics for use in structural damage detection based on changes in modal data. The metaheuristics include differential evolution (DE), artificial bee colony algorithm (ABC), real-code ant colony optimisation (ACOR), charged system search (ChSS), league championship algorithm (LCA), simulated annealing (SA), particle swarm optimisation (PSO), evolution strategies (ES), teaching-learning-based optimisation (TLBO), adaptive differential evolution (JADE), evolution strategy with covariance matrix adaptation (CMAES), success-history based adaptive differential evolution (SHADE) and SHADE with linear population size reduction (L-SHADE). Three truss structures are used to pose several test problems for structural damage detection. The meta-heuristics are then used to solve the test problems treated as optimisation problems. Comparative performance is carried out where the statistically best algorithms are identified.

• Journal of international Conference on Electrical Machines and Systems
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• 제3권2호
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• pp.184-189
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• 2014

In this paper, a new design method of vibration suppression controller for multi-inertia (especially, 3-ineritia) resonance systems is proposed. The controller consists of a digital fuzzy controller for speed loop and a digital PI controller for current minor loop. The three scaling factor of the fuzzy controller and two PI controller gains are determined by Differential Evolution (DE). The DE is one of optimization techniques and a kind of evolutionary computation technique. In this paper, we have applied the DE/rand/1/bin strategy to design the optimal controller parameters. Comparing with the conventional design algorithm, the proposed method is able to shorten the time of the controller design to a large extent and to obtain accurate results. Finally, we confirmed the effectiveness of the proposal method by the computer simulations.

• Journal of Electrical Engineering and Technology
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• 제7권5호
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• pp.681-688
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• 2012

This paper presents the application of Differential Evolution (DE) algorithm to obtain a solution for Bid Based Dynamic Economic Dispatch (BBDED) problem including the transmission losses and to maximize the social profit in a deregulated power system. The IEEE-30 bus test system with six generators, two customers and two trading periods are considered under various bidding strategies in a day-ahead electricity market. By matching the bids received from supplying and distributing entities, the Independent System Operator (ISO) maximize the social profit, (with the choices available). The simulation results of DE are compared with the results of Particle swarm optimization (PSO). The results demonstrate the potential of DE algorithm and show its effectiveness to solve BBDED.

• Industrial Engineering and Management Systems
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• 제14권1호
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• pp.11-21
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• 2015

The purpose of this research is to develop algorithms using the Differential Evolution Algorithm (DE) to solve a multi-objective, sources and stages location-allocation problem. The development process starts from the design of a standard DE, then modifies the recombination process of the DE in order improve the efficiency of the standard DE. The modified algorithm is called modified DE. The proposed algorithms have been tested with one real case study (large size problem) and 2 randomly selected data sets (small and medium size problems). The computational results show that the modified DE gives better solutions and uses less computational time than the standard DE. The proposed heuristics can find solutions 0 to 3.56% different from the optimal solution in small test instances, while differences are 1.4-3.5% higher than that of the lower bound generated by optimization software in medium and large test instances, while using more than 99% less computational time than the optimization software.