• Journal of Electrical Engineering and Technology
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• v.4 no.2
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• pp.175-184
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• 2009

In this paper, a method for optimal measurement placement in the problem of static harmonic state estimation in power systems is proposed. At first, for achieving to a suitable method by considering the precision factor of the estimation, a procedure based on Genetic Algorithm (GA) for optimal placement is suggested. Optimal placement by regarding the precision factor has an evident solution, and the proposed method is successful in achieving the mentioned solution. But, the previous applied method, which is called the Sequential Elimination (SE) algorithm, can not achieve to the evident solution of the mentioned problem. Finally, considering both precision and economic factors together in solving the optimal placement problem, a practical method based on GA is proposed. The simulation results are shown an improvement in the precision of the estimation by using the proposed method.

• Journal of applied mathematics & informatics
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• v.24 no.1_2
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• pp.387-397
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• 2007

According to the controllability of pulse times and the amount of jumps in the states at these times in the process of fed-batch culture producing 1,3-propanediol, this paper proposes a terminal optimal control model, whose constraint condition is the nonlinear impulsive delay system. The existence of optimal control is discussed and an optimization algorithm which is applied to each subinternal over one cycle for this optimal control problem is constructed. Finally, the numerical simulations show that the terminal intensity of producing 1,3-propanediol has been increased obviously.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.8
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• pp.415-422
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• 2000

This paper presents a new algorithm for hierarchical optimal control of nonlinear systems. The proposed method is simple because the solutions are obtained by only exchanging informations of coefficient vector based on interaction prediction principle and FWT(fast Walsh transform) in upper and lower level. Since we solve two point boundary problem with Picard's iterative method and the backward integral operational matrix of Walsh function to obtain the optimal vector of each independent subsystem, the algorithm is simple and its operation is fast without inverse matrix and kronecker product operation. In simulation, the proposed algorithm's usefulness is proved by comparison with the global optimal control methods.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.12
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• pp.43-53
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• 1997

In this paper, the optimal synthesis algorithm of multiple-valued logic circuits using universal logic modules (ULM) U$_{f}$ based on 3-variable ternary reed-muller expansions is presented. We check the degree of each varable for the coefficients of reed-muller expansions and determine the order of optimal control input variables that minimize the number of ULM U$_{f}$ modules. The order of optimal control input variables is utilized the realization of multiple-valued logic circuits to be constructed by ULM U$_{f}$ modules based on reed-muller expansions using the circuit cost matrix. This algorithm is performed only unit time in order to search for the optimal control input variables. Also, this algorithm is able to be programmed by computer and the run time on programming is O(p$^{n}$ ).

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.10
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• pp.702-709
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• 2008

In this paper, the optimal thermal storage time of an air-conditioning system with slab thermal storage in office building was analyzed on the basis of the gradient method algorithm. The sum of room temperature deviation and heat extraction rate was set to the criterion function. It was calculated that four hours is the optimal thermal storage time under the standard evaluation criterion. Furthermore, some case studies were executed by controlling ratio of weight functions of room temperature deviation and heat extraction rate in criterion function. It is possible to design many kinds of optimal operation of an air-conditioning system with slab thermal storage by controlling ratio of the weight functions in criterion function.

• Smart Structures and Systems
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• v.16 no.2
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• pp.243-262
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• 2015

Optimal sensor placement (OSP) is a critical issue in construction and implementation of a sophisticated structural health monitoring (SHM) system. The uncertainties in the identified structural parameters based on the measured data may dramatically reduce the reliability of the condition evaluation results. In this paper, the information entropy, which provides an uncertainty metric for the identified structural parameters, is adopted as the performance measure for a sensor configuration, and the OSP problem is formulated as the multi-objective optimization problem of extracting the Pareto optimal sensor configurations that simultaneously minimize the appropriately defined information entropy indices. The nondirective movement glowworm swarm optimization (NMGSO) algorithm (based on the basic glowworm swarm optimization (GSO) algorithm) is proposed for identifying the effective Pareto optimal sensor configurations. The one-dimensional binary coding system is introduced to code the glowworms instead of the real vector coding method. The Hamming distance is employed to describe the divergence of different glowworms. The luciferin level of the glowworm is defined as a function of the rank value (RV) and the crowding distance (CD), which are deduced by non-dominated sorting. In addition, nondirective movement is developed to relocate the glowworms. A numerical simulation of a long-span suspension bridge is performed to demonstrate the effectiveness of the NMGSO algorithm. The results indicate that the NMGSO algorithm is capable of capturing the Pareto optimal sensor configurations with high accuracy and efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3501-3523
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• 2017

It is a classical integer optimization difficulty to design an optimal selection scheme in cooperative relay networks considering co-channel interference (CCI). In this paper, we solve single-objective and multi-objective relay selection problem. For the single-objective relay selection problem, in order to attain optimal system performance of cooperative relay network, a novel quantum differential evolutionary algorithm (QDEA) is proposed to resolve the optimization difficulty of optimal relay selection, and the proposed optimal relay selection scheme is called as optimal relay selection based on quantum differential evolutionary algorithm (QDEA). The proposed QDEA combines the advantages of quantum computing theory and differential evolutionary algorithm (DEA) to improve exploring and exploiting potency of DEA. So QDEA has the capability to find the optimal relay selection scheme in cooperative relay networks. For the multi-objective relay selection problem, we propose a novel non-dominated sorting quantum differential evolutionary algorithm (NSQDEA) to solve the relay selection problem which considers two objectives. Simulation results indicate that the proposed relay selection scheme based on QDEA is superior to other intelligent relay selection schemes based on differential evolutionary algorithm, artificial bee colony optimization and quantum bee colony optimization in terms of convergence speed and accuracy for the single-objective relay selection problem. Meanwhile, the simulation results also show that the proposed relay selection scheme based on NSQDEA has a good performance on multi-objective relay selection.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.149-154
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• 2018

There is well known algorithm is a Gale-Shapley algorithm(GSA) for stable marriage problem. The GSA is performed as each man propose to his most favorite woman(MP), then the woman accepts more than one proposal rejects all but her favorite from among those who have proposed to her. This algorithm always gets a stable set of marriages with man-optimal and woman-pessimal. But the woman proposal and man-accept/reject method(WP) is can be get the distinct result. Also, the optimal stable matching may be fail using MP or WP. This paper suggests always get the optimal stable matching on all occasions in order to overcome the shortcomings of MP and WP. The proposed algorithm perform k-opt, k-women exchange with each other for the result of delete at less preference in each woman from MP result. As a result of applied to various experimental data, this algorithm can be get the optimal stable matching that the MP or WP failed to it.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.7
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• pp.339-349
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• 2001

This paper suggests an optimal identification method for complex and nonlinear system modeling that is based on Fuzzy-Neural Networks(FNN). The proposed Hybrid Identification Algorithm is based on Yamakawa's FNN and uses the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. In this paper, the FNN modeling implements parameter identification using HCM algorithm and hybrid structure combined with two types of optimization theories for nonlinear systems. We use a HCM(Hard C-Means) clustering algorithm to find initial apexes of membership function. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are adjusted using hybrid algorithm. The proposed hybrid identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregated objective function(performance index) with weighting factor is introduced to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity(distribution of I/O data), we show that it is available and effective to design an optimal FNN model structure with mutual balance and dependency between approximation and generalization abilities. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.11
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• pp.1065-1070
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• 2004

Optimal collision avoidance algorithm for unmanned aerial vehicles based on proportional navigation guidance law is investigated this paper. Although proportional navigation guidance law is widely used in missile guidance problems, it can be used in collision avoidance problem by guiding the relative velocity vector to collision avoidance vector. The optimal navigation coefficient can be obtained if an obstacle if an obstacle moves at constant velocity vector. The stability of the proposed algorithm is also investigated. The stability can be obtained by choosing a proper navigation coefficient.