• Korean Management Science Review
• /
• v.13 no.1
• /
• pp.97-109
• /
• 1996

Genetic Algorithms have been successfully applied to various problems (for example, engineering design problems with a mix of continuous, integer and discrete design variables) that could not have been readily solved with traditional computational techniques. But, several problems for which conventional Genetic Algorithms are ill defined are premature convergence of solution and application of exterior penalty function. Therefore, we developed an Improved Genetic Algorithms (IGAs) to solve above two problems. As a case study, IGAs is applied to several nonlinear optimization problems and it is proved that this algorithm is very useful and efficient in comparison with traditional methods and conventional Genetic Algorithm.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.39 no.8
• /
• pp.792-804
• /
• 1990

This paper describes an efficient optimization algorithm by calculating sensitivity function for power system stabilization. In power system, the dynamic performance of exciter, governor etc. following a disturbance can be presented by a nonlinear differential equation. Since a nonlinear equation can be linearized for small disturbances, the state equation is expressed by a system matrix with system parameters. The objective function for power system operation will be related to the system parameter and the initial state at the optimal control condition for control or stabilization. The object function sensitivity to the system parameter can be considered to be effective in selecting the optimal parameter of the system.

• 제어로봇시스템학회:학술대회논문집
• /
• 1986.10a
• /
• pp.244-247
• /
• 1986

A revised two-level costate prediction algorithm is developed for the optimization of nonlinear nuclear power plant. The algorithm is proved to converge very well, and appears to require substantially small computation time and storage than previous nonlinear optimization algorithm. To cope with unknown external disturbances, we construct a closed loop control system. In order to get a smaller sampling time, this paper proposes the two-level Kalman filter.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2000.05a
• /
• pp.122-125
• /
• 2000

The paper describes the adaptation of evolutionary fuzzy model ins (EFM) in developing global function approximation tools for use in genetic algorithm based optimization of nonlinear structural systems. EFM is an optimization process to determine the fuzzy membership parameters for constructing global approximation model in a case where the training data are not sufficiently provided or uncertain information is included in design process. The paper presents the performance of EFM in terms of numbers of fuzzy rules and training data, and then explores the EFM based sizing of automotive component for passenger protection.

• Journal of Mechanical Science and Technology
• /
• v.20 no.8
• /
• pp.1125-1138
• /
• 2006

The optimal design of the squeeze film damper (SFD) for rotor system has been studied in previous researches. However, these researches have not been considering jumping or nonlinear phenomena of a rotor system with SFD. This paper represents an optimization technique for linear and nonlinear response of a simple rotor system with SFDs by using a hybrid GA-SA algorithm which combined enhanced genetic algorithm (GA) with simulated annealing algorithm (SA). The damper design parameters are the radius, length and radial clearance of the damper. The objective function is to minimize the transmitted load between SFD and foundation at the operating and critical speeds of the rotor system with SFD which has linear and nonlinear unbalance responses. The numerical results show that the transmitted load of the SFD is greatly reduced in linear and nonlinear responses for the rotor system.

• International Journal of Control, Automation, and Systems
• /
• v.1 no.3
• /
• pp.271-281
• /
• 2003

In this paper, a new nonlinear programming approach is suggested to solve biaffine matrix inequality (BMI) problems in multiobjective and structured controls. It is shown that these BMI problems are reduced to nonlinear minimization problems. An algorithm that is easily implemented with existing convex optimization codes is presented for the nonlinear minimization problem. The efficiency of the proposed algorithm is illustrated by numerical examples.

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

• Journal of Power System Engineering
• /
• v.21 no.6
• /
• pp.56-61
• /
• 2017

The check valves are mechanical valves that permit fluids to flow in only one direction, preventing flow from reversing. It is classified as one way directional valves. There are various types of check valves that used in a marine application. A lift type check valve uses the disc to open and close the passage of fluid. The disc lift up from seat as pressure below the disc increases, while drop in pressure on the inlet side or a build up of pressure on the outlet side causes the valve to close. An important concept in check valves is the cracking pressure which is the minimum upstream pressure at which the valve will operate. On the other hand, optimization is a process of finding the best set of parameters to reach a goal while not violating certain constraints. The AMESim software provides NLPQL(Nonlinear Programming by Quadratic Lagrangian) and genetic algorithm(GA) for optimization. NLPQL is the implementation of a SQP(sequential quadratic programming) algorithm. SQP is a standard method, based on the use of a gradient of objective functions and constraints to solve a non-linear optimization problem. A characteristic of the NLPQL is that it stops as soon as it finds a local minimum. Thus, the simulation results may be highly dependent on the starting point which user give to the algorithm. In this paper, we carried out optimization design of the check valve with NLPQL algorithm.

• Industrial Engineering and Management Systems
• /
• v.9 no.1
• /
• pp.10-19
• /
• 2010

This paper presents an extended computing procedure for the global optimization of the triple response system (TRS) where the response functions are nonconvex (nonconcave) quadratics and the input factors satisfy a radial region of interest. The TRS arising from response surface modeling can be approximated using a nonlinear mathematical program involving one primary (objective) function and two secondary (constraints) functions. An optimization algorithm named triple response surface algorithm (TRSALG) is proposed to determine the global optimum for the nondegenerate TRS. In TRSALG, the Lagrange multipliers of target (secondary) functions are computed by using the Hooke-Jeeves search method, and the Lagrange multiplier of the radial constraint is located by using the trust region (TR) method at the same time. To ensure global optimality that can be attained by TRSALG, included is the means for detecting the degenerate case. In the field of numerical optimization, as the family of TR approach always exhibits excellent mathematical properties during optimization steps, thus the proposed algorithm can guarantee the global optimal solution where the optimality conditions are satisfied for the nondegenerate TRS. The computing procedure is illustrated in terms of examples found in the quality literature where the comparison results with a gradient-based method are used to calibrate TRSALG.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.35 no.1
• /
• pp.1-10
• /
• 2011

An optimization method is proposed for preform and billet shape designs in the forging process by using the Equivalent Static Loads (ESLs). The preform shape is an important factor in the forging process because the quality of the final forging is significantly influenced by it. The ESLSO is used to determine the shape of the preform. In the ESLSO, nonlinear dynamic loads are transformed to the ESLs and linear response optimization is performed using the ESLs. The design is updated in linear response optimization and nonlinear analysis is performed with the updated design. The examples in this paper show that optimization using the ESLs is useful and the design results are satisfactory. Consequently, the optimal preform and billet shapes which produce the desired final shape have been obtained. Nonlinear analysis and linear response optimization of the forging process are performed using the commercial software LS-DYNA and NASTRAN, respectively.