• Journal of Power Electronics
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• 제19권5호
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• pp.1248-1258
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• 2019

This paper presents a moth-flame optimization (MFO)-based maximum power point tracking (MPPT) method for photovoltaic (PV) systems. The MFO algorithm is a new optimization method that exhibits satisfactory performance in terms of exploration, exploitation, local optima avoidance, and convergence. Therefore, the MFO algorithm is quite suitable for solving multiple peaks of PV systems under partial shading conditions (PSCs). The proposed MFO-MPPT is compared with four MPPT algorithms, namely the perturb and observe (P&O)-MPPT, incremental conductance (INC)-MPPT, particle swarm optimization (PSO)-MPPT and whale optimization algorithm (WOA)-MPPT. Simulation and experiment results demonstrate that the proposed algorithm can extract the global maximum power point (MPP) with greater tracking speed and accuracy under various conditions.

• Design & Manufacturing
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• 제17권2호
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• pp.62-69
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• 2023

In this study, we conducted a study on the optimization of injection molding conditions to minimize deformation of plastic product. The charging management system housing of the vehicle was selected as the research subject. Melting temperature, cooling temperature, packing time, and packing pressure were selected as the main factors expected to affect the deformation of molded products. Each main factor was divided into 5 levels. Optimization of injection molding conditions to minimize deformation was performed using the Taguchi Method. We performed an analysis of variance (ANOVA) to identify significant factors affecting the deformation of plastic product. In order to select injection molding conditions that minimize deformation of plastic products, injection molding analysis was additionally performed for insignificant factors. We then compared the deformation of the molded part before and after optimization. As a result of comparing the injection analysis results of the basic conditions and the injection analysis results of the optimal conditions, it was confirmed that the amount of deformation after optimization was improved by about 10.9%.

• Journal of Electrical Engineering and Technology
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• 제12권2호
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• pp.643-653
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• 2017

The paper presents controller optimization algorithm for a 12-pulse voltage source converter (VSC) based high voltage direct current (HVDC) system. To get an optimum algorithm, three methods namely conventional-Zeigler-Nichols, linear-golden section search (GSS) and stochastic-particle swarm optimization (PSO) are applied to control of 12 pulse VSC based HVDC system and simulation results are presented to show the best among the three. The performance results are obtained under various dynamic conditions such as load perturbation, non-linear load condition, and voltage sag, tapped load fault at points-of-common coupling (PCC) and single-line-to ground (SLG) fault at input AC mains. The conventional GSS and PSO algorithm are modified to enhance their performances under dynamic conditions. The results of this study show that modified particle swarm optimization provides the best results in terms of quick response to the dynamic conditions as compared to other optimization methods.

• 대한수학회지
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• 제47권1호
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• pp.17-28
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• 2010

In this paper we deal with linear chance-constrained optimization problems, a class of problems which naturally arise in practical applications in finance, engineering, transportation and scheduling, where decisions are made in presence of uncertainty. After giving the deterministic equivalent formulation of a linear chance-constrained optimization problem we construct a conjugate dual problem to it. Then we provide for this primal-dual pair weak sufficient conditions which ensure strong duality. In this way we generalize some results recently given in the literature. We also apply the general duality scheme to a portfolio optimization problem, a fact that allows us to derive necessary and sufficient optimality conditions for it.

• 유통과학연구
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• 제15권2호
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• pp.15-20
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• 2017

Purpose - This paper aims to suggest a delivery constrained internet shopping optimization problem (DISOP) which must be solved for online recommendation system to provide a customized service considering cost and delivery conditions at the same time. Research design, data, and methodology - To solve a (DISOP), we propose a multi-objective formulation and a solution approach. By using a commercial optimization software (LINDO), a (DISOP) can be solved iteratively and a pareto optimal set can be calculated for real-sized problem. Results - We propose a new research problem which is different with internet shopping optimization problem since our problem considers not only the purchasing cost but also delivery conditions at the same time. Furthermore, we suggest a multi-objective mathematical formulation for our research problem and provide a solution approach to get a pareto optimal set by using numerical example. Conclusions - This paper proposes a multi-objective optimization problem to solve internet shopping optimization problem with delivery constraint and a solution approach to get a pareto optimal set. The results of research will contribute to develop a customized comparison and recommendation system to help more easy and smart online shopping service.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제1권1호
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• pp.87-94
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• 2001

Silicon nitride films grown by plasma-enhanced chemical vapor deposition (PECVD) are useful for a variety of applications, including anti-reflecting coatings in solar cells, passivation layers, dielectric layers in metal/insulator structures, and diffusion masks. PECVD systems are controlled by many operating variables, including RF power, pressure, gas flow rate, reactant composition, and substrate temperature. The wide variety of processing conditions, as well as the complex nature of particle dynamics within a plasma, makes tailoring SiN film properties very challenging, since it is difficult to determine the exact relationship between desired film properties and controllable deposition conditions. In this study, SiN PECVD modeling using optimized neural networks has been investigated. The deposition of SiN was characterized via a central composite experimental design, and data from this experiment was used to train and optimize feed-forward neural networks using the back-propagation algorithm. From these neural process models, the effect of deposition conditions on film properties has been studied. A recipe synthesis (optimization) procedure was then performed using the optimized neural network models to generate the necessary deposition conditions to obtain several novel film qualities including high charge density and long lifetime. This optimization procedure utilized genetic algorithms, hybrid combinations of genetic algorithm and Powells algorithm, and hybrid combinations of genetic algorithm and simplex algorithm. Recipes predicted by these techniques were verified by experiment, and the performance of each optimization method are compared. It was found that the hybrid combinations of genetic algorithm and simplex algorithm generated recipes produced films of superior quality.

• Journal of Biosystems Engineering
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• 제29권1호
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• pp.53-58
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• 2004

The purpose of this study was to evaluate an optimization model to determine the operation conditions of the chestnuts flame peeling system. The results of this study were summarized as follows. The optimization model was developed and evaluated to represent the flame peeling characteristics of the domestic chestnuts. When the heating depth was selected for various utilization of the peeled chestnuts, the model could determine the optimal conditions of the hardness of the chestnut shells, the flame temperature, and the flame time to get the maximum peeling ratio of the chestnut flame peeling system. When the heating depth was limited to 2.2 mm, the optimization model determined the proper operation conditions and the maximum peeling ratio such as 1594 g/$\textrm{mm}^2$ of the hardness of the chestnut shells, 780$^{\circ}C$ of the flame temperature, 29 second of the flame time, and 98.1 % of the peeling ratio.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 춘계학술대회 논문집
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• pp.275-280
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• 2004

Shape optimization was performed to obtain the precise shape of cutouts including the internal shape of cutouts in laminated composite plates by three dimensional modeling using solid element. The volume control of the growth-strain method was implemented and the distributed parameter chosen as Tsai-Hill fracture index for shape optimization. The volume control of the growth-strain method makes Tsai-Hill failure index at each element uniform in laminated composites under the initial volume. Then shapes optimized by Tsai-Hill failure index were compared with those of the initial shapes for the various load conditions and cutouts. The following conclusions were obtained in this study. (1) It was found that growth-strain method was applied efficiently to shape optimization of three dimensional cutouts in a laminate composite, (2) The optimal shapes of the various load conditions and cutouts were obtained, (3) The maximum Tsal-Hill failure index was reduced up to 67% when shape optimization was peformed under the initial volume by volume control of growth-strain method.

• 한국생산제조학회지
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• 제26권1호
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• pp.30-35
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• 2017

The major parts of a vacuum cleaner are molded. The vacuum cleaner works in multi-load conditions. Therefore, the designer needs to optimize the structure and injection molding conditions simultaneously. Here, the main factor of design is the rib shape and thickness. The greater the rib thickness, the greater the stiffness of the structure. However, it causes an increase in weight. On the other hand, the lower the rib thickness, the greater the increase in the injection pressure. However, the weight will be reduced. Therefore, the designer needs to optimize the rib shape and thickness for structure stiffness and injection molding. In order to solve this problem, we propose an optimization method using D.O.E and a response surface model, which is a multi-objective optimization method using the multi-objective genetic algorithm.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.822-827
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• 2005

Recently engineering systems problems become quite large and complicated. For those problems, design requirements are fairly complex. It is not easy to design such systems by considering only one discipline. Therefore, we need a design methodology that can consider various disciplines. Multidisciplinary Design Optimization (MDO) is an emerging optimization method to include multiple disciplines. So far, about seven MDO methodologies have been proposed for MDO. They are Multidisciplinary Feasible (MDF), Individual Feasible (IDF), All-at-Once (AAO), Concurrent Subspace Optimization (CSSO), Collaborative Optimization (CO), Bi-Level Integrated System Synthesis (BLISS) and Multidisciplinary Optimization Based on Independent Subspaces (MDOIS). In this research, the performances of the methods are evaluated and compared. Practical engineering problems may not be appropriate for fairness. Therefore, mathematical problems are developed for the comparison. Conditions for fair comparison are defined and the mathematical problems are defined based on the conditions. All the methods are coded and the performances of the methods are compared qualitatively as well as quantitatively.