• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.520-525
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• 2003

The determination of unknown parameters in rotating machinery is a difficult task and optimization techniques represent an alternative technique for parameter identification. The Simulated Annealing(SA) and Genetic Algorithm(GA) are powerful global optimization algorithm. This paper proposes new hybrid algorithm which combined GA with SA and local search algorithm for the purpose of parameter identification. Numerical examples are also presented to verify the efficiency of proposed algorithm. And, this paper presents the general methodology based on hybrid algorithm to identify unknown bearing parameters of flexible rotors using measured unbalance responses. Numerical examples are used to ilustrate the methodology used, which is then validated experimentally.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.629-633
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• 1998

Proposed here is a parallel genetic algorithm accompanied with intermittent migration among subpopulations. It is intended to maintain diversity in the population for a long period . This method was applied to finding out the global maximum of some multimodal functions for which no other methods seem to be useful . Preferable results and their detailed analysis are also presented.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.126-131
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• 2009

This study was carried out for the reliability of PD(Partial Discharge) pattern recognition. For the pattern recognition, the database for PD was established by use of self-designed insulation defects which occur and were mostly critical in GIS(Gas Insulated Switchgear). The acquired database was analyzed to distinguish patterns by means of PRPD(Phase Resolved Partial Discharge) method and stored to the form with to unite the average amplitude of PD pulse and the number of PD pulse as the input data of neural network. In order to prove the performance of genetic algorithm combined with neural network, the neural networks with trial-and-error method and the neural network with genetic algorithm were trained by same training data and compared to the results of their pattern recognition rate. As a result, the recognition success rate of defects was 93.2% and the neural network train process by use of trial-and-error method was very time consuming. The recognition success rate of defects, on the other hand, was 100% by applying the genetic algorithm at neural network and it took a relatively short time to find the best solution of parameters for optimization. Especially, it could be possible that the scrupulous parameters were obtained by genetic algorithm.

• Geomechanics and Engineering
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• v.1 no.1
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• pp.85-96
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• 2009

The radius and coordinate of sliding circle are taken as searching variables in slope stability analysis. Genetic algorithm is applied for searching for critical factor of safety. In order to search for critical factor of safety in slope stability analysis efficiently and in a robust manner, some improvements for simple genetic algorithm are proposed. Taking the advantages of efficiency of neighbor-search of the simulated annealing and the robustness of genetic algorithm, a hybrid optimization method is presented. The numerical computation shows that the procedure can determine the minimal factor of safety and be applied to slopes with any geometry, layering, pore pressure and external load distribution. The comparisons demonstrate that the genetic algorithm provides a same solution when compared with elasto-plastic finite element program.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.175-178
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• 2003

In recent years, several researchers have presented the extensive research reports on network optimization problems. In our real life applications, many important network problems are typically formulated as a Maximum flow model (MXF) or a Minimum Cost flow model (MCF). In this paper, we propose a Genetic Algorithm (GA) approach used a priority-based chromosome for solving the bicriteria network optimization problem including MXF and MCF models(MXF/MCF).

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1997.04a
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• pp.128-132
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• 1997

This paper deals with the optimization of pipe-support allocation using the genetic algorithm, and shows the feasibility of the optimization method to actual design problems and also the convergence characteristics of optimization calculation with respect to the various seismic waves. The piping system was modeled as mass-spring system with 5 degrees of freedom and the support was as spring-damper. The support allocation problem was formulated to minimize the response of the piping system to seismic excitation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.108-113
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• 2002

This paper presents a multi-step structural design optimization method fur machine tool structures using a genetic algorithm with dynamic penalty. The first step is a sectional topology optimization, which is to determine the best sectional construction that minimize the structural weight and the compliance responses subjected to some constraints. The second step is a static design optimization, in which the weight and the static compliance response are minimized under some dimensional and safety constraints. The third step is a dynamic design optimization, where the weight static compliance, and dynamic compliance of the structure are minimized under the same constraints. The proposed design method was examined on the 10-bar truss problem of topology and sizing optimization. And the results showed that our solution is better than or just about the same as the best one of the previous researches. Furthermore, we applied this method to the topology and sizing optimization of a crossbeam slider for a high-speed machining center. The topology optimization result gives the best desirable cross-section shape whose weight was reduced by 38.8% than the original configuration. The subsequent static and dynamic design optimization reduced the weight, static and dynamic compliances by 5.7 %, 2.1% and 19.1% respectively from the topology-optimized model. The examples demonstrated the feasibility of the suggested design optimization method.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.2
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• pp.129-134
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• 2008

In this paper, we carry out the simulation for an optimal solution of one-dimensional nonlinear photonic crystal structure using Genetic algorithm, and show the proposed method to apply for photonic transistors. Unlike a conventional steepest descent method for an optimization, the proposed method based on Genetic Algorithm has advantages for finding out excellent solutions without any analytic forms, which can easily apply to other applications. Also, as several solutions around global minimum solution can be obtained, it is very good optimization tool to give us the patterns about the optimal structure of a photonic crystal transistor. To design an all-optical filter transistor, Neural network algorithm is firstly performed for an initial design and then Genetic Algorithm is finally used to get the optimal solution. From the simulation of one-dimensional photonic crystal transistor, 27dB of the switching On/Off ratio is obtained.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.902-907
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• 2004

In this paper, a multi-step optimization using a G.A. (Genetic Algorithm) with variable penalty function is introduced to the structural design optimization of a 5-head route machine. Our design procedure consist of two design optimization stage. The first stage of the design optimization is static design optimization. The following stage is dynamic design optimization stage. In the static optimization stage, the static compliance and weight of the structure are minimized simultaneously under some dimensional constraints and deflection limits. On the other hand, the dynamic compliance and the weight of the machine structure are minimized simultaneously in the dynamic design optimization stage. As the results, dynamic compliance of the 5-head router machine was decreased by about 37％ and the weight of the structure was decreased by 4.48％ respectively compared with the simplified structure model.

• Journal of Mechanical Science and Technology
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• v.20 no.1
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• pp.133-146
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• 2006

Collaborative optimization (CO) is a multi-level decomposed methodology for a large-scale multidisciplinary design optimization (MDO). CO is known to have computational and organizational advantages. Its decomposed architecture removes a necessity of direct communication among disciplines, guaranteeing their autonomy. However, CO has several problems at convergence characteristics and computation time. In this study, such features are discussed and some suggestions are made to improve the performance of CO. Only for the system level optimization, genetic algorithm is used and gradient-based method is used for subspace optimizers. Moreover, response surface models are replaced as analyses in subspaces. In this manner, CO is applied to aero-structural design problems of the aircraft wing and its results are compared with the multidisciplinary feasible (MDF) method and the original CO. Through these results, it is verified that the suggested approach improves convergence characteristics and offers a proper solution.