• Journal of Korea Water Resources Association
• /
• v.37 no.2
• /
• pp.121-131
• /
• 2004

SIMHYD and TANK model are used to predict time series of daily rainfall-runoff of Soyang Dam and Youngcheon Dam watershed. The performances of SIMHYD model with 7 parameters and TANK model with17 parameters are compared. Three optimization methods (Genetic algorithm, Pattern search multi-start and Shuffled Complex Evolution algorithm) were applied to study-areas with 3 different types of objective functions. Efficiency of TANK model is higher than that of SIMHYD. Among different types of objective function, Nash-sutcliffe coefficient is found to be the most appropriateobjective function to evaluate applicability of model.

• Journal of Environmental Impact Assessment
• /
• v.22 no.6
• /
• pp.713-724
• /
• 2013

In terms of waste load allocation, inequality of waste load discharge must be considered as well as economic aspects such as minimization of waste load abatement. The inequality of waste load discharge between areas was calculated with Gini coefficient and was included as one of the objective functions of the multi-objective waste load allocation. In the past, multi-objective functions were usually weighted and then transformed into a single objective optimization problem. Recently, however, due to the difficulties of applying weighting factors, multi-objective genetic algorithms (GA) that require only one execution for optimization is being developed. This study analyzes multi-objective waste load allocation using NSGA-II-aJG that applies Pareto-dominance theory and it's adaptation of jumping gene. A sensitivity analysis was conducted for the parameters that have significant influence on the solution of multi-objective GA such as population size, crossover probability, mutation probability, length of chromosome, jumping gene probability. Among the five aforementioned parameters, mutation probability turned out to be the most sensitive parameter towards the objective function of minimization of waste load abatement. Spacing and maximum spread are indexes that show the distribution and range of optimum solution, and these two values were the optimum or near optimal values for the selected parameter values to minimize waste load abatement.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.16 no.5
• /
• pp.91-98
• /
• 2017

This study was conducted to optimize the spring constant and the damping coefficient, which are design parameters of the tractor cabin suspension system, to minimize the ride vibration. A 3D tractor MBD (multi-body dynamics) model with a cabin suspension system was developed using a dynamic analysis program (Recurdyn). Using the developed model and optimization algorithm, the spring constant and the damping coefficient, which are the design parameters of the cabin suspension for the tractor, was were optimized so thatto minimize the maximum overshoot for the vertical displacement of the cabin was minimized. The percent maximum overshoot of the tractor cabin was simulated for the 13 initial models, which were obtained using the ISCD-II method, and for the 3 additional SAO models presented in the optimization algorithm software. The model that represents with the smallest percent maximum overshoot among the 16 models was selected as the optimized model. The percent maximum overshoot of the optimized model was about approximately 5% lower than that of the existing model.

• Geomechanics and Engineering
• /
• v.36 no.2
• /
• pp.111-119
• /
• 2024

In the realm of rock excavation projects, precise estimation of the drilling rate index stands as a pivotal factor in strategic planning and cost assessment. This study introduces and evaluates two pioneering computational intelligence models designed for the prognostication of the drilling rate index, a pivotal parameter with direct implications for cost estimation in rock excavation projects. These models, denoted as the Relevance Vector Regression (RVR) optimized with the Invasive Weed Optimization algorithm (IWO) (RVR-IWO model) and the RVR integrated with the Shuffled Frog Leaping algorithm (SFL) (RVR-SFL model), represent a groundbreaking approach to forecasting drilling rate index. The RVR-IWO and RVR-SFL models were meticulously devised to harness the capabilities of computational intelligence and optimization techniques for drilling rate index estimation. This research pioneers the integration of IWO and SFL with RVR, constituting an unprecedented effort in forecasting drilling rate index. The primary objective of this study was to gauge the precision and dependability of these models in forecasting the drilling rate index, revealing significant distinctions between the two. In terms of predictive precision, the RVR-IWO model emerged as the superior choice when compared to the RVR-SFL model, underscoring the remarkable efficacy of the Invasive Weed Optimization algorithm. The RVR-IWO model delivered noteworthy results, boasting a Variance Account for (VAF) of 0.8406, a Mean Squared Error (MSE) of 0.0114, and a Squared Correlation Coefficient (R²) of 0.9315. On the contrary, the RVR-SFL model exhibited slightly lower precision, yielding an MSE of 0.0160, a VAF of 0.8205, and an R2 of 0.9120. These findings serve to highlight the potential of the RVR-IWO model as a formidable instrument for drilling rate index prediction, particularly within the framework of rock excavation projects. This research not only makes a significant contribution to the realm of drilling engineering but also underscores the broader adaptability of the RVR-IWO model in tackling an array of challenges within the domain of rock engineering. Ultimately, this study advances the comprehension of drilling rate index estimation and imparts valuable insights into the practical implementation of computational intelligence methodologies within the realm of engineering projects.

• Steel and Composite Structures
• /
• v.45 no.6
• /
• pp.775-796
• /
• 2022

In this study, a new type of energy absorbers with a functionally graded thickness is investigated, these type of absorbers absorb energy through expanding-folding processes. The expanding-folding absorbers are composed of two sections: a thin-walled aluminum matrix and a thin-walled steel mandrel. Previous studies have shown higher efficiency of the mentioned absorbers compared to the conventional ones. In this study, the effect of thickness which has been functionally-graded on the aluminum matrix (in which expansion occurs) was investigated. To this end, initial functions were considered for the matrix thickness, which was ascending/descending along the axis. The study was done experimentally and numerically. Comparing the experimental data with the numerical results showed high consistency between the numerical and experimental results. In the final section of this study, the best energy absorber functionally graded thickness was introduced by optimization using a third-order genetic algorithm. The optimization results showed that by choosing a minimum thickness of 1.6 mm and the exponential coefficient of 3.25, the most optimal condition can be obtained for descending thickness absorbers.

• Journal of information and communication convergence engineering
• /
• v.20 no.2
• /
• pp.103-112
• /
• 2022

The goal of pattern mining is to identify novel patterns in a database. High utility itemset mining (HUIM) is a research direction for pattern mining. This is different from frequent itemset mining (FIM), which additionally considers the quantity and profit of the commodity. Several algorithms have been used to mine high utility itemsets (HUIs). The original BPSO algorithm lacks local search capabilities in the subsequent stage, resulting in insufficient HUIs to be mined. Compared to the transfer function used in the original PSO algorithm, the V-shaped transfer function more sufficiently reflects the probability between the velocity and position change of the particles. Considering the influence of the acceleration factor on the particle motion mode and trajectory, a nonlinear acceleration strategy was used to enhance the search ability of the particles. Experiments show that the number of mined HUIs is 73% higher than that of the original BPSO algorithm, which indicates better performance of the proposed algorithm.

• Journal of Power Electronics
• /
• v.19 no.5
• /
• pp.1315-1325
• /
• 2019

Aiming at the negative sequence and harmonic problems in the operation of railway static power conditioners, an optimization compensation strategy for negative sequence and harmonics is studied in this paper. First, the hybrid RPC topology and compensation principle are analyzed to obtain different compensation zone states and current capacities. Second, in order to optimize the RPC capacity configuration, the minimum RPC compensation capacity is calculated according to constraint conditions, and the optimal compensation coefficient and compensation angle are obtained. In addition, the voltage unbalance ${\varepsilon}_U$ and power factor requirements are satisfied. A PSO (Particle Swarm Optimization) algorithm is used to calculate the three indexes for minimum compensating energy. The proposed method can precisely calculate the optimal compensation capacity in real time. Finally, MATLAB simulations and an experimental platform verify the effectiveness and economics of the proposed algorithm.

• The KSFM Journal of Fluid Machinery
• /
• v.2 no.1 s.2
• /
• pp.29-34
• /
• 1999

To develop the efficient numerical optimization method for the design of an airfoil, an evaluation of various methods coupled with two-dimensional Naviev-Stokes analysis is presented. Simplex method and Hook-Jeeves method we used as direct search methods, and steepest descent method, conjugate gradient method and DFP method are used as indirect search methods and are tested to determine the search direction. To determine the moving distance, the golden section method and cubic interpolation method are tested. The finite volume method is used to discretize two-dimensional Navier-Stokes equations, and SIMPLEC algorithm is used for a velocity-pressure correction method. For the optimal design of two-dimensional airfoil, maximum thickness, maximum ordinate of camber line and chordwise position of maximum ordinate are chosen as design variables, and the ratio of drag coefficient to lift coefficient is selected as an objective function. From the results, it is found that conjugate gradient method and cubic interpolation method are the most efficient for the determination of search direction and the moving distance, respectively.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.3
• /
• pp.466-472
• /
• 2008

The paper concerns Fuzzy C-Means clustering based fuzzy neural networks (FCM-FNN) and the optimization of the network is carried out by means of hierarchal fair competition-based parallel genetic algorithm (HFCPGA). FCM-FNN is the extended architecture of Radial Basis Function Neural Network (RBFNN). FCM algorithm is used to determine centers and widths of RBFs. In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values directly rely on the computation of the relevant distance between data points by means of FCM. Also, as the consequent part of fuzzy rules extracted by the FCM-FNN model, the order of four types of polynomials can be considered such as constant, linear, quadratic and modified quadratic. Since the performance of FCM-FNN is affected by some parameters of FCM-FNN such as a specific subset of input variables, fuzzification coefficient of FCM, the number of rules and the order of polynomials of consequent part of fuzzy rule, we need the structural as well as parametric optimization of the network. In this study, the HFCPGA which is a kind of multipopulation-based parallel genetic algorithms(PGA) is exploited to carry out the structural optimization of FCM-FNN. Moreover the HFCPGA is taken into consideration to avoid a premature convergence related to the optimization problems. The proposed model is demonstrated with the use of two representative numerical examples.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.41 no.7
• /
• pp.509-515
• /
• 2013

In this study, the real-coded adaptive range multi-objective genetic algorithm code, which represents the global multi-objective optimization algorithm, was developed for an airfoil shape design. In order to achieve the better aerodynamic characteristics than reference airfoil at landing and cruise conditions, maximum lift coefficient and lift-to-drag ratio were chosen as object functions. Futhermore, the PARSEC method reflecting geometrical properties of airfoil was adopted to generate airfoil shapes. Finally, two airfoils, which show better aerodynamic characteristics than a reference airfoil, were chosen. As a result, maximum lift coefficient and lift-to-drag ratio were increased of 4.89% and 5.38% for first candidate airfoil and 7.13% and 4.33% for second candidate airfoil.