• Journal of Electrical Engineering and Technology
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• v.8 no.1
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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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• 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.

• Korean Journal of Computational Design and Engineering
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• v.19 no.4
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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.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.451-461
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• 2002

Differential evolution(DE) has been preyed to be an efficient method for optimizing real-valued multi-modal objective functions. DE's main assets are its conceptual simplicity and ease of use. However, the convergence properties are deeply dependent on the control parameters of DE. This paper proposes an adaptive differential evolution(ADE) method which combines with a variant of DE and an adaptive mechanism of the control parameters. ADE contributes to the robustness and the easy use of the DE without deteriorating the convergence. 12 optimization problems is considered to test ADE. As an application of ADE the paper presents a supervised clustering method for predicting events, what is called, an evolutionary event clustering(EEC). EEC is tested for 4 cases used widely for the validation of data modeling.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.679-683
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• 2007

Statistical learning theory by Vapnik consists of support vector machine(SVM), support vector regression(SVR), and support vector clustering(SVC) for classification, regression, and clustering respectively. In this algorithms, SVC is good clustering algorithm using support vectors based on Gaussian kernel function. But, similar to SVM and SVR, SVC needs to determine kernel parameters and regularization constant optimally. In general, the parameters have been determined by the arts of researchers and grid search which is demanded computing time heavily. In this paper, we propose a differential evolution based SVC(DESVC) which combines differential evolution into SVC for efficient selection of kernel parameters and regularization constant. To verify improved performance of our DESVC, we make experiments using the data sets from UCI machine learning repository and simulation.

• Bulletin of the Korean Mathematical Society
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• v.34 no.2
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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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• v.1 no.4
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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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• v.3 no.2
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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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• v.7 no.5
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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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• v.14 no.1
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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.