• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.120-122
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• 2006

The paper concerns the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation. The granulation is realized with the aid of the Hard C-means clustering and HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy model. It concerns the fuzzy model-related parameters such as the number of input variables to be used, a collection of specific subset of input variables, the number of membership functions, the order of polynomial, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. Thestructural optimization is realized via HFCGA and HCM method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.3 s.174
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• pp.661-666
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• 2000

Optimization of the elastic joint of train is performed according to the minimization of ten responses which represent driving safety and ride comfort of train and analyzed by using the each response se surface model from stochastic design of experiments. After the each response surface model is constructed, the main effect and sensitivity analyses are successfully performed by 2nd order approximated regression model as described in this paper. We can get the optimal solutions using by nonlinear programming method such as simplex or interval optimization algorithms. The response surface models and the optimization algorithms are used together to obtain the optimal design of the elastic joint of train. the ten 2nd order polynomial response surface models of the three translational stiffness of the elastic joint (design factors) are constructed by using CCD(Central Composite Design) and the multi-objective optimization is also performed by applying min-max and distance minimization techniques of relative target deviation.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.1
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• pp.113-120
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• 2013

This paper proposes a design optimization approach for core of solenoid actuators by combining optimization techniques with the finite element method (FEM). A solenoid is an important element part which hydraulically controls a transmission system, etc. The demanded feature of the solenoid is that it performs an electromagnetic force output being constant regardless of the stroke and being proportional to coil current. The plunger compresses a spring with a minimum force of 12 N over an 1.7 mm travel. The orthogonal array, analysis of variance (ANOVA) techniques and response surface optimization, are employed to determine the main effects and their optimal design variables. The methodology is demonstrated as a optimization tool for the core design of a solenoid actuator.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.2552-2557
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• 2007

Shape optimization of an internal cooling passage with staggered dimples on single surface is performed and performances of surrogates are evaluated in this paper. Optimizations are performed so that turbulent heat transfer can be enhanced compromising with pressure loss due to friction. The three-dimensional governing differential equations have been solved to find the overall Nusselt number and friction factor which are related to the objective functions of this problem. Three design variables were selected among the dimensionless geometric variables. Basic surrogate models such as second order polynomial response surface approximation (RSA), Kriging meta-modeling technique, radial basis neural network (RBNN), and derived press based averaged (PBA) surrogate model are constructed. The optimal points are searched from the above constructed surrogates by sequential quadratic programming (SQP). It is shown that use of multiple surrogates can increase the robustness in prediction of better design with minimum computational cost.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.22 no.4
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• pp.811-825
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• 1998

An approximate line search is presented for dynamic response optimization with Augmented Lagrange Multiplier(ALM) method. This study empolys the approximate a augmented Lagrangian, which can improve the efficiency of the ALM method, while maintaining the global convergence of the ALM method. Although the approximate augmented Lagragian is composed of only the linearized cost and constraint functions, the quality of this approximation should be good since an approximate penalty term is found to have almost second-order accuracy near the optimum. Typical unconstrained optimization algorithms such as quasi-Newton and conjugate gradient methods are directly used to find exact search directions and a golden section method followed by a cubic polynomial approximation is empolyed for approximate line search since the approximate augmented Lagrangian is a nonlinear function of design variable vector. The numberical performance of the proposed approach is investigated by solving three typical dynamic response optimization problems and comparing the results with those in the literature. This comparison shows that the suggested approach is robust and efficient.

• Journal of the Society of Naval Architects of Korea
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• v.40 no.5
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• pp.26-35
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• 2003

Propellers operating in a given nonuniform ship wake generate unsteady loads leading to undesirable stern vibration problems. The skew is known to be the most proper and effective geometric parameter to control or reduce the fluctuating forces on the shaft. This paper assumes the skew profile as either a quadratic or a cubic function of the radius and determines the coefficients of the polynomial function by applying the simplex method. The method uses the converted unconstrained algorithm to solve the constrained minimization problem of 6-component shaft excitation forces. The propeller excitation was computed either by applying the two-dimensional gust theory for quick estimation or by the fully three-dimensional unsteady lifting surface theory in time domain for an accurate solution. A sample result demonstrates that the shaft forces can be further reduced through optimization from the original design.

• Journal of Pharmaceutical Investigation
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• v.18 no.1
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• pp.31-41
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• 1988

Computer optimization technique was applied to obtain the optimum formula of o/w type ointment vehicle containing sodium lauryl sulfate and 1-methyl-2-pyrrolidinone (MP). In order to determine the feasibility of optimizing a vehicle composition with the aid of computer, the amounts of sodium lauryl sulfate $(X_1)$, salicylic acid $(X_2)$, and MP $(X_3)$ were selected as the independent variables for the solubility and the absorption rates of salicylic acid (dependent variables). The experimental values of absorption rates agreed well with the calculation values obtained from the polynomial regression analysis, and the contour charts drawn by computer were useful in optimization process.

• Journal of the Korea Society of Computer and Information
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• v.21 no.2
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• pp.97-103
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• 2016

This paper deals with the cycle time minimization problem that determines the productivity in printed circuit board (PCB) with n components using the m placement machines. This is known as production cycle time determination problem (PCTDP). The polynomial time algorithm to be obtain the optimal solution has been unknown yet, therefore this hard problem classified by NP-complete. This paper gets the initial assignment result with the machine has minimum unit placement time per each component firstly. Then, the balancing process with reallocation from overhead machine to underhead machine. Finally, we perform the swap optimization and get the optimal solution of cycle time $T^*$ within O(mn) computational complexity. For experimental data, the proposed algorithm can be obtain the same result as integer programming+branch-and-bound (IP+B&B) and B&B.

• Journal of KSNVE
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• v.10 no.1
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• pp.123-128
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• 2000

This paper describes the optimization of the hexagonal array transducer using finite element method. The transducer consists of the disc type sensors. Three dimensional beam patterns of each element and the array transducer are analysed using the finite element code ATILA. Beam patterns were analyzed for the disc type transducer. To optimize beam patterns of the array transducer, Chebyshev polynomial weight is applied to each element. In case of applying optimized weight, a 30 degree width beam pattern is presented at 10kHz. This paper also includes the effect of rubber filling material instead of using the water inside the transducer array.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2627-2629
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• 2003

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN). The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized networks, and to be much more flexible and preferable neural network than the conventional SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented with using nonlinear system data.