• Proceedings of the KSME Conference
• /
• 2001.06d
• /
• pp.771-776
• /
• 2001

By matching a well established fast throughflow code, with standard loss correlations, and an efficient optimization algorithm, a new design system has been developed, which optimizes inlet and exit flow-field parameters for each blade row of a multistage axial flow turbine. The compressible steady state inviscid throughflow code based on streamline curvature method is suitable for fast and accurate flow calculation and performance prediction of a multistage axial flow turbine. A general purpose hybrid constrained optimization package, iSIGHT has been used, which includes the following modules: genetic algorithm, simulated annealing, modified method of feasible directions. The design system has been demonstrated using an example of a 5-stage low pressure steam turbine for 800MW thermal power plant previously designed by HANJUNG. The comparison of computed performance of initial and optimized design shows significant improvement in the turbine efficiency.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.8 no.1
• /
• pp.127-137
• /
• 2002

This paper deals with developing a Web-based Solver NRO(Network Reliability Optimizer) for solving three classes of reliability redundancy optimization problems which are generated in series systems. parallel systems and complex systems. Inputs of NRO consisted in four parts. that is, user authentication. system selection. input data and confirmation. After processing of inputs through internet, NRO provides conveniently the optimal solutions for the given problems on the Web-site. To alleviate the risks of being trapped in a local optimum, HH(Hybrid-Heuristic) algorithm is incorporated in NRO for solving the given three classes of problems, and moderately combined GA(Genetic Algorithm) with the modified SA(Simulated Annealing) algorithm.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.396-396
• /
• 2000

Multiperiod optimization is the key step to tackle the supply chain optimization problems. Taking supply and demand uncertainty or prediction into consideration during the process synthesis phase leads to the maximization of the profit for the long range time horizon. In this study, new algorithm based on the Genetic Algorithms is proposed for multiperiod optimization formulated in MINLP, GDP and hybrid MINLP/GDP. In this study, the focus is given especially on the design of the Genetic Algorithm suitable to handle disjunctive programming with the same level of MINLP handling capability. Hybridization with the Simulated Annealing is tried. and many heuristics are adopted for this purpose.

• Journal of Power System Engineering
• /
• v.11 no.1
• /
• pp.20-27
• /
• 2007

In this paper, finite element modeling was performed for vibration analysis of a rotor system installed in sunroof motor, and analysis process was developed for natural frequency and unbalance response analysis. At the same time, to reduce analysis modeling error caused by the difference between analysis and measured values, finite element model updating was conducted using an optimization algorithm, i.e. hybrid genetic algorithm and simulated annealing (HGASA) method. For this end experimental modal test was carried out and by using the measured frequency response function (FRF), model updating was performed considering both cases where core coil was removed and included. And acceptable result was obtained. Also, dynamic property coefficient of bush bearing which influences vibration response of the rotor system was estimated.

• Journal of information and communication convergence engineering
• /
• v.5 no.1
• /
• pp.35-41
• /
• 2007

In this paper, a modified Fuzzy C-Means (MFCM) algorithm is presented for nonlinear blind channel equalization. The proposed MFCM searches the optimal channel output states of a nonlinear channel, based on the Bayesian likelihood fitness function instead of a conventional Euclidean distance measure. In its searching procedure, all of the possible desired channel states are constructed with the elements of estimated channel output states. The desired state with the maximum Bayesian fitness is selected and placed at the center of a Radial Basis Function (RBF) equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with that of a hybrid genetic algorithm (GA merged with simulated annealing (SA): GASA), and the relatively high accuracy and fast searching speed are achieved.

• Nuclear Engineering and Technology
• /
• v.29 no.1
• /
• pp.68-77
• /
• 1997

The reactor power control system is described in the fashion of the order increased LQR system. To obtain the optimal state feedback gain vectors, the weighting matrix of the performance function should be determined. Since the contentional method has some limitations, stochastic searching methods are investigated to optimize the LQR weighting matrix using the modified genetic algorithm combined with the simulated annealing, a new optimizing tool named the hybrid MGA-SA is developed to determine the weighting parameters of the LQR system. This optimizing tool provides a more systematic approach in designing the LQR system. Since it can be easily incorporated with any forms of the cost function, it also provides the great flexibility in the optimization problems.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2008.04a
• /
• pp.416-421
• /
• 2008

A Metropolis genetic algorithm (MGA) is a newly-developed hybrid algorithm combining simple genetic algorithm (SGA) and simulated annealing (SA). In the algorithm, favorable features of Metropolis criterion of SA are incorporated in the reproduction operations of SGA. This way, MGA alleviates the disadvantages of finding imprecise solution in SGA and time-consuming computation in SA. It has been successfully applied and the efficiency has been verified for the practical structural design optimization. However, applicability of MGA for the wider range of problems should be rigorously proved through the solution of mathematical optimization problems. Thus, performances of MGA for the typical mathematical problems are investigated and compared with those of conventional algorithms such as SGA, micro genetic algorithm (${\mu}GA$), and SA. And, for better application of MGA, the effects of acceptance level are also presented. From numerical Study, it is again verified that MGA is more efficient and robust than SA, SGA and ${\mu}GA$ in the solution of mathematical optimization problems having various features.

• Journal of Korea Multimedia Society
• /
• v.11 no.12
• /
• pp.1625-1634
• /
• 2008

In this study, a modified Fuzzy C-Means algorithm with Gaussian weights (MFCM_GW) is presented for the problem of nonlinear blind channel equalization. The proposed algorithm searches for the optimal channel output states of a nonlinear channel based on received symbols. In contrast to conventional Euclidean distance in Fuzzy C-Means (FCM), the use of the Bayesian likelihood fitness function and the Gaussian weighted partition matrix is exploited in this method. In the search procedure, all possible sets of desired channel states are constructed by considering the combinations of estimated channel output states. The set of desired states characterized by the maxima] value of the Bayesian fitness is selected and updated by using the Gaussian weights. After this procedure, the Bayesian equalizer with the final desired states is implemented to reconstruct transmitted symbols. The performance of the proposed method is compared with those of a simplex genetic algorithm (GA), a hybrid genetic algorithm (GA merged with simulated annealing (SA):GASA), and a previously developed version of MFCM. In particular, a relative]y high accuracy and a fast search speed have been observed.

• Geophysics and Geophysical Exploration
• /
• v.12 no.1
• /
• pp.99-104
• /
• 2009

We propose a waveform inversion method for SH-wave data obtained in a shallow seismic refraction survey, to determine a 2D inhomogeneous S-wave profile of shallow soils. In this method, a 2.5D equation is used to simulate SH-wave propagation in 2D media. The equation is solved with the staggered grid finite-difference approximation to the 4th-order in space and 2nd-order in time, to compute a synthetic wave. The misfit, defined using differences between calculated and observed waveforms, is minimised with a hybrid heuristic search method. We parameterise a 2D subsurface structural model with blocks with different depth boundaries, and S-wave velocities in each block. Numerical experiments were conducted using synthetic SH-wave data with white noise for a model having a blind layer and irregular interfaces. We could reconstruct a structure including a blind layer with reasonable computation time from surface seismic refraction data.

• The Journal of the Acoustical Society of Korea
• /
• v.22 no.7
• /
• pp.562-572
• /
• 2003

A matched-target model inversion method was developed for a passive sonar to estimate the position of moving targets. Based on the well known matched-field processing in underwater acoustics, the method finds target position by matching the measured target directions and frequencies with the corresponding values of the proposed target model. For the efficient and accurate estimations, the parameter searching was accomplished using a hybrid optimizing method, which first starts with a global optimization such as generic algorithm or simulated annealing then applies a local optimization of a simple down hill algorithm. The suggested method was testified using simulations for three different moving scenarios. The simulation results showed that the method is robust in convergence, even under the situation of over 5 times standard deviation of Gaussian distribution of measured error, and is practical in calculation time as well.