• 한국정보통신설비학회:학술대회논문집
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• 2005.08a
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• pp.115-119
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• 2005

In this paper, we propose a method that search the sweet spot of array antenna beam, and keep it for fast speed transmission in millimeter wave on single array antenna link. We use TDD(Time Division Duplex) as transfer method, and it transfers the control data of antenna. The proposed method is the modified genetic algorithm which selects a superior initial group through slave-processing in order to resolve the local solution of genetic algorithm. The efficiency of the proposed method is verified by means of simulations with white Gaussian noise and not on single array antenna link.

• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2115-2123
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• 2006

The paper deals with the identification of Pareto optimal solutions using GA based coevolution in the context of multiobjective optimization. Coevolution is a genetic process by which several species work with different types of individuals in parallel. The concept of cooperative coevolution is adopted to compensate for each of single objective optimal solutions during genetic evolution. The present study explores the GA based coevolution, and develops prescribed and adaptive scheduling schemes to reflect design characteristics among single objective optimization. In the paper, non-dominated Pareto optimal solutions are obtained by controlling scheduling schemes and comparing each of single objective optimal solutions. The proposed strategies are subsequently applied to a three-bar planar truss design and an energy preserving flywheel design to support proposed strategies.

• Journal of applied mathematics & informatics
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• v.28 no.3_4
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• pp.597-610
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• 2010

For a class of nonlinear bilevel programming problems in which the follower's problem is linear, the paper develops a genetic algorithm based on the optimality conditions of linear programming. At first, we denote an individual by selecting a base of the follower's linear programming, and use the optimality conditions given in the simplex method to denote the follower's solution functions. Then, the follower's problem and variables are replaced by these optimality conditions and the solution functions, which makes the original bilevel programming become a single-level one only including the leader's variables. At last, the single-level problem is solved by using some classical optimization techniques, and its objective value is regarded as the fitness of the individual. The numerical results illustrate that the proposed algorithm is efficient and stable.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.1
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• pp.61-73
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• 2012

The paper presents the novel approach to solve some problems occurred in application of the genetic algorithm to the determination of the optimal tire pattern sequence in order to reduce the tire air-pumping noise which is generated by the repeated compression and expansion of the air cavity between tire pattern and road surface. The genetic algorithm has been used to find the optimal tire pattern sequence having a low level of tire air-pumping noise using the image based air-pumping model. In the genetic algorithm used in the previous researches, there are some problems in the encoding structure and the selection of objective function. The paper proposed single encoding element with five integers, divergent objective function based on evolutionary process and the optimal evolutionary rate based on Shannon entropy to solve the problems. The results of the proposed genetic algorithm with evolutionary process are compared with those of the randomized algorithm without evolutionary process on the two-dimensional normal distribution. It is confirmed that the genetic algorithm is more effective to reduce the peak value of the predicted tire air-pumping noise and the consistency and cohesion of the obtained simulation results are also improved in terms of probability.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.825-836
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• 2003

Based on customer information and financing processes in capital market, we derived individual models by applying multi-layered perceptrons, MDA, and decision tree. Further, the results from the existing single models were compared with the results from the integrated model that was developed using genetic algorithm. This study contributes not only to verifying the existing individual models and but also to overcoming the limitations of the existing approaches. We have depended upon the approaches that compare individual models and search for the best-fit model. However, this study presents a methodology to build an integrated data mining model using genetic algorithm.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1835-1844
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• 2022

Single-orifice plate is wildly utilized in the piping system of the nuclear power plant to throttle and depressurize the fluid of the pipeline. The cavitation induced by the single-orifice plate may cause some serious vibration of the pipeline. This study aims to find the optimal designs of the single-orifice plates that may have weak cavitation possibilities. For this purpose, a new single-orifice plate with a convergent-flat-divergent hole was modeled, a multi-objective optimization method was proposed to optimize the shape of a single-orifice plate, while computational fluid dynamics method was adopted to obtain the fluid physical quantities. The reciprocal cavitation number and the developmental integral were treated as cavitation indexes (e.g., objectives for the optimization algorithm). Two non-dominant designs ultimately achieved illustrated obvious reduction in the cavitation indexes at a Reynolds number Re = 1 ×10⁵ defined based on fluid velocity. Besides, the sensitivity analysis and temperature effects were also performed. The results indicated that the convergent angle of the single-orifice plate dominants the cavitation behavior globally. The optimal designs of single-orifice plates result in lower downstream jet areas and lower upstream pressure. For a constant Reynolds number, the higher temperature of liquid water, the easier it is to undergo cavitation. Whereas there is a diametric phenomenon for a constant fluid velocity. Moreover, the regression models were carried out to establish the mathematical relation between temperature and cavitation indexes.

• Industrial Engineering and Management Systems
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• v.12 no.3
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• pp.181-189
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• 2013

This paper proposes a two-phase genetic algorithm (GA) to solve the problem of obtaining an optimum configuration of a paired single row assembly line. We pair two single-row assembly lines due to the shared usage of several workstations, thus obtaining an optimum configuration by considering the material flow of the two rows simultaneously. The problem deals with assigning workstations to a sequence and selecting the best arrangement by looking at the length and width for each workstation. This can be considered as an enhancement of the single row facility layout problem (SRFLP), or the so-called paired SRFLP (PSRFLP). The objective of this PSRFLP is to find an optimal configuration that seeks to minimize the distance traveled by the material handler and even the use of the material handler itself if this is possible. Real-world applications of such a problem can be found for apparel, shoe, and other manual assembly lines. This research produces the schematic representation solution using the heuristic approach. The crossover and mutation will be utilized using the schematic representation solution to obtain the neighborhood solutions. The first phase of the GA result is recorded to get the best pair. Based on these best matched pairs, the second-phase GA can commence.

• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.1
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• pp.101-114
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• 1996

This study suggests an accelerated genetic algorithm for the vehicle routing problem (AGAVRP). This algorithm treats both the single-visit and the multiple-visit models. AGAVRP is accelerated by the OR techniques at the various stages of the algorithm. In order to improve the convergence of AGAVRP, a robust set of parameters is determined by the experimental design approach. The relative performance of AGAVRP is comparable to the other known algorithms. The advantage of the proposed algorithm is flexibility and better convergence.

• Journal of international Conference on Electrical Machines and Systems
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• v.1 no.1
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• pp.135-142
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• 2012

In this paper, optimal selection of the unified power flow controller (UPFC) damping controller parameters in order to improve the power system dynamic response and its stability based on two modified intelligent algorithms have been proposed. These algorithms are based on a modified intelligent particle swarm optimization (PSO) and continuous genetic algorithm (GA). After extraction of UPFC dynamic model, intelligent PSO and genetic algorithms are used to select the effective feedback signal of the damping controller; then, to compare the performance of the proposed UPFC controller in damping the critical modes of a single-machine infinite-bus (SMIB) power system, the simulation results are presented. The comparison shows the good performance of both presented PSO and genetic algorithms in an optimal selection of UPFC damping controller parameters and damping oscillations.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.3
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• pp.30-39
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• 2010

This paper considers a problem of optimizing torch paths to cut stock plates nested with open contours. For each contour, one of the two ending points is to be selected as a starting point of cutting with the other being the exit point. A torch path is composed of a single depot and a series of starting and ending points of contours to be cut. The torch path optimization problem is shown to be formulated as an extended version of the standard travelling salesman problem. To solve the problem, a hybrid genetic algorithm with the local search of torch paths is proposed. The genetic algorithm is tested for hypothetical problems whose optimal solutions are known in advance due to the special structure of them. The computational results show that the algorithm generates very near optimal solutions for most cases of the test problems, which verifies the validity of the algorithms.