• Structural Engineering and Mechanics
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• v.42 no.3
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• pp.335-352
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• 2012

Particle Swarm Optimization (PSO) is a stochastic population based optimization algorithm which has attracted attentions of many researchers. This method has great potentials to be applied to many optimization problems. Despite its robustness the standard version of PSO has some drawbacks that may reduce its performance in optimization of complex structures such as laminated composites. In this paper by suggesting a new variation scheme for acceleration parameters and inertial weight factors of PSO a novel optimization algorithm is developed to enhance the basic version's performance in optimization of laminated composite structures. To verify the performance of the new proposed method, it is applied in two multi-objective design optimization problems of laminated cylindrical. The numerical results from the proposed method are compared with those from two other conventional versions of PSO-based algorithms. The convergancy of the new algorithms is also compared with the other two versions. The results reveal that the new modifications inthe basic forms of particle swarm optimization method can increase its convergence speed and evade it from local optima traps. It is shown that the parameter variation scheme as presented in this paper is successful and can evenfind more preferable optimum results in design of laminated composite structures.

• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.136-147
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• 2003

To achieve an optimal performance for a given problem, we need an integrative process of the parameter optimization via learning and the structure optimization via model selection. In this paper, we propose an efficient optimization method for improving generalization performance by considering the property of each sub-method and by combining them with common theoretical properties. First, weight parameters are optimized by natural gradient teaming with adaptive regularization, which uses a diverse error function. Second, the network structure is optimized by eliminating unnecessary parameters with natural pruning. Through iterating these processes, candidate models are constructed and evaluated based on the Bayesian Information Criterion so that an optimal one is finally selected. Through computational experiments on benchmark problems, we confirm the weight parameter and structure optimization performance of the proposed method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.11
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• pp.1361-1370
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• 2012

A shape optimization is proposed to obtain the desired final shape of forming and forging products in the manufacturing process. The final shape of a forming product depends on the shape parameters of the initial blank shape. The final shape of a forging product depends on the shape parameters of the billet shape. Shape optimization can be used to determine the shape of the blank and billet to obtain the appropriate final forming and forging products. The equivalent static loads method for non linear static response structural optimization (ESLSO) is used to perform metal forming and forging optimization since nonlinear dynamic analysis is required. Stress equivalent static loads (stress ESLs) are newly defined using a virtual model by redefining the value of the material properties. The examples in this paper show that optimization using the stress ESLs is quite useful and the final shapes of a forming and forging products are identical to the desired shapes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.1-11
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• 2018

In various engineering fields, determining the appropriate parameter set is a cumbersome and difficult task when solving optimization problems. Despite the appropriate parameter setting through parameter sensitivity analysis, there are limits to evaluating whether the parameters are appropriate for all optimization problems. For this reason, kinds of a Self-adaptive Harmony searches have been developed to solve various engineering problems by the appropriate setting of algorithm's own parameters according to the problem. In this study, various types of Self-adaptive Harmony searches were investigated and the characteristics of optimization were categorized. Six algorithms with a differentiation of optimization process were applied and compared with not only the mathematical optimization problem, but also the engineering problem, which has been applied widely in the algorithm performance comparisons. The performance of each algorithm was compared, and the statistical performance indicators were used to evaluate the application results quantitatively.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.395-395
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• 2000

Optimization of existing processes becomes more important than the past as environmental problems and concerns about energy savings stand out. When we can model a process mathematically, we can easily optimize it by using the model as constraints. However, modeling is very difficult for most chemical processes as they include numerous units together with their correlation and we can hardly obtain parameters. Therefore, optimization that is based on the process models is, in turn, hard to perform. Especially, f3r unknown processes, such as bioprocess or microelectronics materials process, optimization using mathematical model (first principle model) is nearly impossible, as we cannot understand the inside mechanism. Consequently, we propose a few optimization method using empirical model evolutionarily instead of mathematical model. In this method, firstly, designing experiments is executed fur removing unecessary experiments. D-optimal DOE is the most developed one among DOEs. It calculates design points so as to minimize the parameters variances of empirical model. Experiments must be performed in order to see the causation between input variables and output variables as only correlation structure can be detected in historical data. And then, using data generated by experiments, empirical model, i.e. response surface is built by PLS or MLR. Now, as process model is constructed, it is used as objective function for optimization. As the optimum point is a local one. above procedures are repeated while moving to a new experiment region fur finding the global optimum point. As a result of application to the pulp digester benchmark model, kappa number that is an indication fur impurity contents decreased to very low value, 3.0394 from 29.7091. From the result, we can see that the proposed methodology has sufficient good performance fur optimization, and is also applicable to real processes.

• International Journal of Aeronautical and Space Sciences
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• v.13 no.1
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• pp.43-57
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• 2012

In a modern aircraft, there are many variations in its mass, stiffness, and aerodynamic characteristics. Recently, an analytical approach was proposed, and this approach uses the idea of uncertainty to find out the most critical flight flutter boundary due to the variations in such aerodynamic characteristics. An analytical method that has been suggested to predict robust stability is the mu method. We previously analyzed the robust flutter boundary by using the mu method, and in that study, aerodynamic variations in the Mach number, atmospheric density, and flight speed were taken into consideration. The authors' previous attempt and the results are currently quoted as varying Mach number mu analysis. In the author's previous method, when the initial flight conditions were located far from the nominal flutter boundary, conservative predictions were obtained. However, relationships among those aerodynamic parameters were not applied. Thus, the varying Mach number mu analysis results required validation. Using an optimization approach, the varying Mach number mu analysis was found out to be capable of capturing a reasonable robust flutter boundary, i.e., with a low percentage difference from boundaries that were obtained by optimization. Regarding the optimization approach, a discrete nominal flutter boundary is to be obtained in advance, and based on that boundary, an interpolated function was established. Thus, the optimization approach required more computational effort for a larger number of uncertainty variables. And, this produced results similar to those from the mu method which had lower computational complexity. Thus, during the estimation of robust aeroelastic stability, the mu method was regarded as more efficient than the optimization method was. The mu method predicts reasonable results when an initial condition is located near the nominal flutter boundary, but it does not consider the relationships that are among the aerodynamic parameters, and its predictions are not very accurate when the initial condition is located far from the nominal flutter boundary. In order to provide predictions that are more accurate, the relationships among the uncertainties should also be included in the mu method.

• Structural Engineering and Mechanics
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• v.51 no.4
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• pp.547-564
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• 2014

This study focuses on the application of an active tuned mass damper (ATMD) for controlling the seismic response of an 11-story building. The control action is achieved by combination of a fuzzy logic controller (FLC) and Particle Swarm Optimization (PSO) method. FLC is used to handle the uncertain and nonlinear phenomena while PSO is used for optimization of FLC parameters. The FLC system optimized by PSO is called PSFLC. The optimization process of the FLC system has been performed for an 11-story building under the earthquake excitations recommended by International Association of Structural Control (IASC) committee. Minimization of the top floor displacement has been used as the optimization criteria. The results obtained by the PSFLC method are compared with those obtained from ATMD with GFLC system which is proposed by Pourzeynali et al. and non-optimum FLC system. Based on the parameters obtained from PSFLC system, a global controller as PSFLCG is introduced. Performance of the designed PSFLCG has been checked for different disturbances of far-field and near-field ground motions. It is found that the ATMD system, driven by FLC with the help of PSO significantly reduces the peak displacement of the example building. The results show that the PSFLCG decreases the peak displacement of the top floor by about 10%-30% more than that of the FLC system. To show the efficiency and superiority of the adopted optimization method (PSO), a comparison is also made between PSO and GA algorithms in terms of success rate and computational processing time. GA is used by Pourzeynali et al for optimization of the similar system.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.12
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• pp.953-960
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• 2020

This paper presents an optimization of gear web design used in a main gear train of an engine reduction gearbox for a rotorcraft. The optimization involves the minimization of a total weight, transmission error, misalignment, and face load distribution factor. In particular, three design variables such as a gear web thickness, location of rim-web connection, and location of shaft-web connection were set as design parameters. In the optimization process, web, rim and shaft of gears were converted from the 3D CAD geometry model to the finite element model, and then provided as input to the gear simulation program, MASTA. Lastly, NSGA-II optimization method was used to find the best combination of design parameters. As a result of the optimization, the total weight, transmission error, misalignment, face load distribution factor were all reduced, and the maximum stress was also shown to be a safe level, confirming that the overall gear performance was improved.

• Transactions of Materials Processing
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• v.9 no.3
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• pp.284-292
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• 2000

Optimization of the process parameters is carried out for process design in sheet metal forming processes. The scheme incorporates with a rigid-plastic finite element method for the deformation analysis and response surface methodology for the optimum searching of process parameters. The algorithm developed is applied to design of the draw bead force and the die radius in deep drawing processes of rectangular cups. The present algorithm shows the capability of designing process parameters which enable the prevention of the weak part of fracture during processes.

• Journal of Power System Engineering
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• v.19 no.5
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• pp.25-31
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• 2015

This study deals with optimal conditions for design parameters of the multistage bollard with up and down installed on the street to protect pedestrians or stop cars. FE simulation and Taguchi method are used to achieve the optimization for the automatic multistage bollard to minimize effective stress caused by the external force. Thickness, height of stage 2, diameter and over-all height which affect its structural strength are chosen as design parameters. According to the experiments combined by orthogonal array, each of the effective stresses is evaluated. And the results are analyzed by using the signal to noise ratio concept of Taguchi method. From their results, the optimal combination of design parameters are proposed.