• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.6 s.249
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• pp.691-697
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• 2006

Metamodel, model of model, has been widely used to improve an efficiency of optimization process in engineering fields. However, global metamodels of constraints in a constrained optimization problem are required good accuracy around neighborhood of optimum point. To satisfy this requirement, more sampling points must be located around the boundary and inside of feasible region. Therefore, a new sampling strategy that is capable of identifying feasible domain should be applied to select sampling points for metamodels of constraints. In this research, we suggeste sequential feasible domain sampling that can locate sampling points likely within feasible domain by using penalty function method. To validate the excellence of feasible domain sampling, we compare the optimum results from the proposed method with those form conventional global space-filling sampling for a variety of optimization problems. The advantages of the feasible domain sampling are discussed further.

• Journal of Korean Institute of Industrial Engineers
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• v.12 no.1
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• pp.107-118
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• 1986

A heuristic algorithm (SIMICOM) has been designed and tested for optimizing simulated stochastic systems whose performances are functions of several discrete decision variables. The approach adopted utilizes an integer complex method coupled with techniques of establishing confidence intervals for the system's responses. It can handle a general class of optimization problems that could be constrained or unconstrained. In constrained cases, the constraints could either be explicit analytical functions of decision variables or be expressed as other responses of the simulation model. In addition to obtain a reasonably accurate solution, the economic aspect of obtaining the solution has also been taken into consideration.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.4
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• pp.363-370
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• 2014

A new NURBS-based shape optimization method is proposed. Most shape optimization studies consider only control point positions as design variables. Some shape optimization processes present problems with mesh quality and convergence when control points are constrained to a limited space. If the weights of the control points are regarded as additional design variables, it should be possible to attain a better degree of shape control. In this study, positions and weights of NURBS control points are used as design variables, and a shape optimization algorithm incorporates position optimization and weight optimization steps. This method is applied to shape optimization benchmarking problems to verify its advantages.

• Bulletin of the Korean Mathematical Society
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• v.38 no.3
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• pp.565-573
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• 2001

In this paper, we first rove the strict quasi-concavity of maximizing function, and next prove a new maximum theorem using Fan’s generalization of the classical KKM theorem. Also an existence theorem of social equilibrium can be proved when an additional assumption on the constraint correspondence is assumed. Finally, we give illustrative two examples of constrained optimization problems.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.490-495
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• 1992

A neural model predictive control strategy combining a neural network for plant identification and a nonlinear programming algorithm for solving nonlinear control problems is proposed. A constrained nonlinear optimization approach using successive quadratic programming cooperates with neural identification network is used to generate the optimum control law for the complicate continuous/batch chemical reactor systems that have inherent nonlinear dynamics. Based on our approach, we developed a neural model predictive controller(NMPC) which shows excellent performances on nonlinear, model-plant mismatch cases of chemical reactor systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.899-902
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• 1993

A neural model predictive control strategy combining a neural network for plant identification and a nonlinear programming algorithm for solving nonlinear control problems is proposed. A constrained nonlinear optimization approach using successive quadratic programming combined with neural identification network is used to generate the optimum control law for complex continuous chemical reactor systems that have inherent nonlinear dynamics. The neural model predictive controller (MNPC) shows good performances and robustness. To whom all correspondence should be addressed.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.10
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• pp.480-488
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• 2001

This paper presents a technique that can obtain an optimal solution for the Security-Constrained Economic Dispatch (SCED) problems using the Interior Point Method (IPM) while taking into account of the power flow constraints. The SCED equations are formulated by using only the real power flow equations from the optimal power flow. Then an algorithm is presented that can linearize the SCED equations based on the relationships among generation real power outputs, loads, and transmission losses to obtain the optimal solutions by applying the linear programming (LP) technique. The objective function of the proposed linearization algorithm are formulated based on the fuel cost functions of the power plants. The power balance equations utilize the Incremental Transmission Loss Factor (ITLF) corresponding to the incremental generation outputs and the line constraints equations are linearized based on the Generalized Generation Distribution Factor (GGDF). Finally, the application of the Primal Interior Point Method (PIPM) for solving the optimization problem based on the proposed linearized objective function is presented. The results are compared with the Simplex Method and the promising results ard obtained.

• Journal of Pharmaceutical Investigation
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• v.6 no.2
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• pp.69-82
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• 1976

Tablet product design problem was structured as constrained optimization problem and subsequently solved by multiple regression analysis and Lagrangian method of optimization. We used Lagrangian method for the purpose of finding the reason of the previous results. Biodiastase and cellulase were the enzymes, chosen, $Avicel{\circledR}$ and corn starch or calcium carboxy methyl cellulose were the binder and disintegrant, respectively. The effect of the dry binder and disintegrant concentration on tablet hardness, friability, volume, disintegration time was recorded. Optimization of this parameter was studied by using the constrained optimization method. In addition to finding a optimal condition of the enzyme tablets, the application of sensitivity analysis studies to such problems was also illustrated. In order to get a stable preparations of the enzyme tablets, accelerated test of coating tablets was carried out in this study. the results are as follows. 1) The minimum disintegration time, such that the average tablet volume did not exceed 0.0154 cubic inch and the average friability value did not exceed 0.62%, was 6.6 minutes and then $Avicel{\circledR}$ and corn starch were 15.4% and 17.2%, respectively. 2) The multiple-correlation coefficients for the regression models of tablet hardness, friability, disintegration time and volume were with in the 95% confidence range. 3) According to the test results, calcium carboxymethyl cellulose can be used as a disintegrant instead of corn starch.

• Journal of Electrical Engineering and Technology
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• v.2 no.4
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• pp.413-419
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• 2007

The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, it has been intensively studied and widely used in power system operation and planning. In the past few decades, many stochastic optimization methods such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm Optimization (PSO) have been applied to solve the OPF problem. In particular, PSO is a newly proposed population based stochastic optimization algorithm. The main idea behind it is based on the food-searching behavior of birds and fish. Compared with other stochastic optimization methods, PSO has comparable or even superior search performance for some hard optimization problems in real power systems. Nowadays, some modifications such as breeding and selection operators are considered to make the PSO superior and robust. In this paper, we propose the Modified PSO (MPSO), in which the mutation operator of GA is incorporated into the conventional PSO to improve the search performance. To verify the optimal solution searching ability, the proposed approach has been evaluated on an IEEE 3D-bus test system. The results showed that performance of the proposed approach is better than that of the standard PSO.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.79-86
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• 2020

To make a satisfactory decision regarding project scheduling, a trade-off between the resource-related cost and project duration must be considered. A beneficial method for decision makers is to provide a number of alternative schedules of diverse project duration with minimum resource cost. In view of optimization, the alternative schedules are Pareto sets under multi-objective of project duration and resource cost. Assuming that resource cost is closely related to resource leveling, a heuristic algorithm for resource capacity reduction (HRCR) is developed in this study in order to generate the Pareto sets efficiently. The heuristic is based on the fact that resource leveling can be improved by systematically reducing the resource capacity. Once the reduced resource capacity is given, a schedule with minimum project duration can be obtained by solving a resource-constrained project scheduling problem. In HRCR, VNS (Variable Neighborhood Search) is implemented to solve the resource-constrained project scheduling problem. Extensive experiments to evaluate the HRCR performance are accomplished with standard benchmarking data sets, PSPLIB. Considering 5 resource leveling objective functions, it is shown that HRCR outperforms well-known multi-objective optimization algorithm, SPEA2 (Strength Pareto Evolutionary Algorithm-2), in generating dominant Pareto sets. The number of approximate Pareto optimal also can be extended by modifying weight parameter to reduce resource capacity in HRCR.