• Communications of the Korean Mathematical Society
• /
• v.25 no.1
• /
• pp.139-147
• /
• 2010

A generalized nondifferentiable fractional optimization problem (GFP), which consists of a maximum objective function defined by finite fractional functions with differentiable functions and support functions, and a constraint set defined by differentiable functions, is considered. Recently, Kim et al. [Journal of Optimization Theory and Applications 129 (2006), no. 1, 131-146] proved optimality theorems and duality theorems for a nondifferentiable multiobjective fractional programming problem (MFP), which consists of a vector-valued function whose components are fractional functions with differentiable functions and support functions, and a constraint set defined by differentiable functions. In fact if $\overline{x}$ is a solution of (GFP), then $\overline{x}$ is a weakly efficient solution of (MFP), but the converse may not be true. So, it seems to be not trivial that we apply the approach of Kim et al. to (GFP). However, modifying their approach, we obtain optimality conditions and duality results for (GFP).

• The Transactions of the Korean Institute of Electrical Engineers B
• /
• v.51 no.12
• /
• pp.697-703
• /
• 2002

Interior permanent magnet synchronous motor(IPMSM) is widely used in many applications such as an electric vehicle, compressor drives of air conditioner and machine tool spindle drives. In order to maximize the efficiency in such applications, this paper is proposed the optimal control method of the armature current. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the optimal control of the armature current. The minimization of loss is possible to realize efficiency optimization control for the proposed IPMSM The optimal current can be decided according to the operating speed and the load conditions. The proposed control algorithm is applied to IPMSM drive system, the operating characteristics controlled by efficiency optimization control are examined in detail by simulation.

• Computers and Concrete
• /
• v.2 no.5
• /
• pp.345-366
• /
• 2005

This work presents an assessment of the computational performance of a vector-parallel implementation of probabilistic model for concrete cracking in 3D. This paper shows the continuing efforts towards code optimization as reported in earlier works Paz, et al. (2002a,b and 2003). The probabilistic crack approach is based on the direct Monte Carlo method. Cracking is accounted by means of 3D interface elements. This approach considers that all nonlinearities are restricted to interface elements modeling cracks. The heterogeneity governs the overall cracking behavior and related size effects on concrete fracture. Computational kernels in the implementation are the inexact Newton iterative driver to solve the non-linear problem and a preconditioned conjugate gradient (PCG) driver to solve linearized equations, using an element by element (EBE) strategy to compute matrix-vector products. In particular the paper analyzes code behavior using OpenMP directives in parallel vector processors (PVP), such as the CRAY SV1 and CRAY T94. The impact of the memory architecture on code performance, and also some strategies devised to circumvent this issue are addressed by numerical experiment.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.9
• /
• pp.2976-2990
• /
• 2022

The adaptive adjustment of reference or weight vectors in decomposition-based methods has been a hot research topic in the evolutionary community over the past few years. Although various methods have been proposed regarding this issue, most of them aim to diversify solutions in the objective space to cover the true Pareto fronts as much as possible. Different from them, this paper proposes a knee point-based reference vector adaptive adjustment strategy to concurrently balance the convergence and diversity. To be specific, the knee point-based reference vector adaptive adjustment strategy firstly utilizes knee points to construct the adaptive reference vectors. After that, a new fitness function is defined mathematically. Then, this paper further designs a many-objective evolutionary algorithm with knee point-based reference vector adaptive adjustment strategy, where the mating operation and environmental selection are designed accordingly. The proposed method is extensively tested on the WFG test suite with 8, 10 and 12 objectives and MPDMP with state-of-the-art optimizers. Extensive experimental results demonstrate the superiority of the proposed method over state-of-the-art optimizers and the practicability of the proposed method in tackling practical many-objective optimization problems.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.28 no.1
• /
• pp.63-69
• /
• 2015

In this paper, infrared reflector design targeting infrared stealth effect is presented using structural optimization based on the phase field method. The analysis model was determined to accomplish the design that an incident infrared wave was reflected to a desired direction. The design process was to maximize the objective value at the measuring domain located in a target region and the design objective was set to the Poynting vector value which represents the energy flux. Optimization results were obtained according to the variation of some parameter values related to the phase field method. The model with a maximum objective value was selected as the final optimal model. The optimal model was modified to eliminate the gray scale using the cut-off method and it confirmed improved performance. In addition, to check the desired effect in the middle wave infrared range(MWIR), the analysis was performed by changing the input wavelength. The finite element analysis and optimization process were performed by using the commercial package COMSOL combined with the Matlab programming.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.30 no.4
• /
• pp.537-544
• /
• 2020

Advances in detection techniques, such as mutation and obfuscation, are being advanced with the development of malware technology. In the malware detection technology, unknown malware detection technology is important, and a method for Malware Authorship Attribution that detects an unknown malicious code by identifying the author through distributed malware is being studied. In this paper, we try to extract the compiler information affecting the binary-based author identification method and to investigate the sensitivity of feature selection, probability and non-probability models, and optimization to classification efficiency between studies. In the experiment, the feature selection method through information gain and the support vector machine, which is a non-probability model, showed high efficiency. Among the optimization studies, high classification accuracy was obtained through feature selection and model optimization through the proposed framework, and resulted in 48% feature reduction and 53 faster execution speed. Through this study, we can confirm the sensitivity of feature selection, model, and optimization methods to classification efficiency.

• Computers and Concrete
• /
• v.32 no.6
• /
• pp.577-594
• /
• 2023

Engineered cementitious composites with calcined clay limestone cement (LC³-ECC) as a kind of green, low-carbon and high toughness concrete, has recently received significant investigation. However, the complicated relationship between potential influential factors and LC³-ECC compressive strength makes the prediction of LC³-ECC compressive strength difficult. Regarding this, the machine learning-based prediction models for the compressive strength of LC³-ECC concrete is firstly proposed and developed. Models combine three novel meta-heuristic algorithms (golden jackal optimization algorithm, butterfly optimization algorithm and whale optimization algorithm) with support vector regression (SVR) to improve the accuracy of prediction. A new dataset about LC³-ECC compressive strength was integrated based on 156 data from previous studies and used to develop the SVR-based models. Thirteen potential factors affecting the compressive strength of LC³-ECC were comprehensively considered in the model. The results show all hybrid SVR prediction models can reach the Coefficient of determination (R²) above 0.95 for the testing set and 0.97 for the training set. Radar and Taylor plots also show better overall prediction performance of the hybrid SVR models than several traditional machine learning techniques, which confirms the superiority of the three proposed methods. The successful development of this predictive model can provide scientific guidance for LC³-ECC materials and further apply to such low-carbon, sustainable cement-based materials.

• 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.

• Journal of Computing Science and Engineering
• /
• v.9 no.2
• /
• pp.39-50
• /
• 2015

Genetic algorithm (GA) and particle swarm optimization (PSO) are two excellent approaches to multimodal optimization problems. However, slow convergence or premature convergence readily occurs because of inappropriate and inflexible evolution. In this paper, a novel optimization algorithm with a flexible grid optimization (FGO) is suggested to provide adaptive trade-off between exploration and exploitation according to the specific objective function. Meanwhile, a uniform agents array with adaptive scale is distributed on the gird to speed up the calculation. In addition, a dominance centroid and a fitness center are proposed to efficiently determine the potential guides when the population size varies dynamically. Two types of subregion division strategies are designed to enhance evolutionary diversity and convergence, respectively. By examining the performance on four benchmark functions, FGO is found to be competitive with or even superior to several other popular algorithms in terms of both effectiveness and efficiency, tending to reach the global optimum earlier. Moreover, FGO is evaluated by applying it to a parameter decision in a least squares support vector machine (LS-SVM) to verify its practical competence.

• Journal of Broadcast Engineering
• /
• v.10 no.3
• /
• pp.270-285
• /
• 2005

A joint design scheme is proposed to optimize the up/down scaler and the motion vector estimation module in the transcoder system. The proposed scheme first optimizes the resolution scaler for a fixed motion vector, and then a new motion vector is estimated for the fixed scaler. These two steps are iteratively repeated until they reach a local optimum solution. In the optimization of the scaler, we derive an adaptive version of a cubic convolution interpolator to enlarge or reduce digital images by arbitrary scaling factors. The adaptation is performed at each macroblock of an image. In order to estimate the optimal motion vector, a temporary motion vector is composed from the given motion vectors. Then the motion vector is refined over a narrow search range. It is well-known that this refinement scheme provides the comparable performance compared to the full search method. Simulation results show that a jointly optimized system based on the proposed algorithms outperforms the conventional systems. We can also see that the algorithms exhibit significant improvement in the minimization of information loss compared with other techniques.