• Proceedings of the KSME Conference
• /
• 2001.11a
• /
• pp.533-538
• /
• 2001

This paper describes a combinational method to compute the global and local solutions of optimization problems. The present hybrid algorithm uses both a genetic algorithm and a local concentrate search algorithm (e. g simplex method). The hybrid algorithm is not only faster than the standard genetic algorithm but also supplies a more accurate solution. In addition, this algorithm can find the global and local optimum solutions. The present algorithm can be supplied to minimize the resonance response (Q factor) and to yield the critical speeds as far from the operating speed as possible. These factors play very important roles in designing a rotor-bearing system under the dynamic behavior constraint. In the present work, the shaft diameter, the bearing length, and clearance are used as the design variables.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.26 no.6
• /
• pp.1008-1015
• /
• 2002

This paper proposes a combinatorial method to compute the global and local solutions of optimization problem. The present hybrid algorithm is the synthesis of a genetic algorithm and a local concentrate search algorithm (simplex method). The hybrid algorithm is not only faster than the standard genetic algorithm, but also gives a more accurate solution. In addition, this algorithm can find both the global and local optimum solutions. An optimization result is presented to demonstrate that the proposed approach successfully focuses on the advantages of global and local searches. Three numerical examples are also presented in this paper to compare with conventional methods.

• Journal of Industrial Technology
• /
• v.20 no.B
• /
• pp.27-34
• /
• 2000

The definition of flatness is given by ISO, ANSI, KS, etc. but those standards don't mention about the specific methods for the flatness. So various solution models that are based on the Minimum Zone Method have been proposed as an optimization problem for the minimax curve fitting. But it has been rare to compare some optimization algorithms to make a guideline for choosing better algorithms in this field. Hence this paper examined and compared Genetic Algorithm and Simplex Method to the evaluation of flatness. As a result, Genetic Algorithm gave the better or equal flatness than Simplex Method but it has the inefficiency caused from the large number of iteration. Therefore, in the future, another researches about alternative algorithms including Hybrid Genetic Algorithm should be achieved to improve the efficiency of Genetic Algorithm for the evaluation of flatness.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.1 no.1
• /
• pp.87-94
• /
• 2001

Silicon nitride films grown by plasma-enhanced chemical vapor deposition (PECVD) are useful for a variety of applications, including anti-reflecting coatings in solar cells, passivation layers, dielectric layers in metal/insulator structures, and diffusion masks. PECVD systems are controlled by many operating variables, including RF power, pressure, gas flow rate, reactant composition, and substrate temperature. The wide variety of processing conditions, as well as the complex nature of particle dynamics within a plasma, makes tailoring SiN film properties very challenging, since it is difficult to determine the exact relationship between desired film properties and controllable deposition conditions. In this study, SiN PECVD modeling using optimized neural networks has been investigated. The deposition of SiN was characterized via a central composite experimental design, and data from this experiment was used to train and optimize feed-forward neural networks using the back-propagation algorithm. From these neural process models, the effect of deposition conditions on film properties has been studied. A recipe synthesis (optimization) procedure was then performed using the optimized neural network models to generate the necessary deposition conditions to obtain several novel film qualities including high charge density and long lifetime. This optimization procedure utilized genetic algorithms, hybrid combinations of genetic algorithm and Powells algorithm, and hybrid combinations of genetic algorithm and simplex algorithm. Recipes predicted by these techniques were verified by experiment, and the performance of each optimization method are compared. It was found that the hybrid combinations of genetic algorithm and simplex algorithm generated recipes produced films of superior quality.

• Structural Engineering and Mechanics
• /
• v.80 no.5
• /
• pp.539-551
• /
• 2021

With the continuous increase of computational capacity, more and more complex nonlinear elastoplastic constitutive models were developed to study the mechanical behavior of elastoplastic materials. These constitutive models generally contain a large amount of physical and phenomenological parameters, which often require a large amount of computational costs to determine. In this paper, an inverse parameter determination method is proposed to identify the constitutive parameters of elastoplastic materials, with the consideration of both strain rate effect and temperature effect. To carry out an efficient design, a hybrid optimization algorithm that combines the genetic algorithm and the Nelder-Mead simplex algorithm is proposed and developed. The proposed inverse method was employed to determine the parameters for an elasto-viscoplastic constitutive model and Johnson-cook model, which demonstrates the capability of this method in considering strain rate and temperature effect, simultaneously. This hybrid optimization algorithm shows a better accuracy and efficiency than using a single algorithm. Finally, the predictability analysis using partial experimental data is completed to further demonstrate the feasibility of the proposed method.

• Structural Engineering and Mechanics
• /
• v.32 no.5
• /
• pp.667-683
• /
• 2009

FE model-based dynamic analysis has been widely used to predict the dynamic characteristics of civil structures. In a physical point of view, an FE model is unavoidably different from the actual structure as being formulated based on extremely idealized engineering drawings and design data. The conventional model updating methods such as direct method and sensitivity-based parameter estimation are not flexible for model updating of complex and large structures. Thus, it is needed to develop a model updating method applicable to complex structures without restriction. The main objective of this paper is to present the model updating method based on the hybrid genetic algorithm (HGA) by combining the genetic algorithm as global optimization method and modified Nelder-Mead's Simplex method as local optimization method. This FE model updating method using HGA does not need the derivation of derivative function related to parameters and without application of complicated inverse analysis methods. In order to allow its application on diversified and complex structures, a commercial FEA tool is adopted to exploit previously developed element library and analysis algorithms. Moreover, an output-level objective function making use of measurement and analytical results is also presented to update simultaneously the stiffness and mass of the analysis model. The numerical examples demonstrated that the proposed method based on HGA is effective for the updating of the FE model of bridge structures.

• Journal of Korea Multimedia Society
• /
• v.9 no.12
• /
• pp.1689-1699
• /
• 2006

A nonlinear channel blind equalizer by using a hybrid genetic algorithm, which merges a genetic algorithm with simulated annealing, and a RBF network is presented. In this study, a hybrid genetic algorithm is used to estimate the output states of a nonlinear channel, based on the Bayesian likelihood fitness function, instead of the channel parameters. From these estimated output states, the desired channel states of the nonlinear channel are derived and placed at the center of a 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 those of a conventional genetic algorithm(GA) and a simplex GA, and the relatively high accuracy and fast convergence of the method are achieved.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2001.11b
• /
• pp.805-809
• /
• 2001

This paper is presented to determine the optimal parameters of squeeze film damper using an enhanced genetic algorithm (EGA). The damper design parameters are the radius, length and radial clearance of the damper. The objective function is minimization of a transmitted load between bearing and foundation at the operating and critical speeds of a flexible rotor. The present algorithm was the synthesis of a genetic algorithm with simplex method for a local concentrate search. This hybrid algorithm is not only faster than the standard genetic algorithm, but also gives a more accurate solution and can find both the global and local optimum solution. The numerical example is presented that illustrated the effectiveness of enhanced genetic algorithm for the optimal design of the squeeze film damper for reducing transmitted load.

• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2008.03a
• /
• pp.469-475
• /
• 2008

The design of a scramjet inlet is a process to search global optimization results among those factors influencing the geometry of scramjet in their ranges for some requirements. An optimization algorithm of hybrid genetic algorithm based on genetic algorithm and simplex algorithm was established for this purpose. With the sample provided by a uniform method, the compressive angles which also are wedge angles of the inlet were chosen as the inlet design variables, and the drag coefficient, total pressure recovery coefficient, pressure rising ratio and the combination of these three variables are designed specifically as different optimization objects. The contrasts of these four optimization results show that the hybrid genetic algorithm developed in this paper can capably implement the optimization process effectively for the inlet design and demonstrate some good adaptability.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.4 no.3
• /
• pp.259-265
• /
• 2004

In this study, a hybrid genetic algorithm merged with simulated annealing is presented to solve nonlinear channel blind equalization problems. The equalization of nonlinear channels is more complicated one, but it is of more practical use in real world environments. The proposed hybrid genetic algorithm with simulated annealing is used to estimate the output states of nonlinear channel, based on the Bayesian likelihood fitness function, instead of the channel parameters. By using the desired channel states derived from these estimated output states of the nonlinear channel, the Bayesian equalizer is implemented 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 those of a conventional genetic algorithm(GA) and a simplex GA. In particular, we observe a relatively high accuracy and fast convergence of the method.