• Structural Engineering and Mechanics
• /
• v.57 no.1
• /
• pp.161-178
• /
• 2016

This research is aimed to design and analyze the performance of double dynamic vibration absorber (DVA) using a pendulum and a spring-mass type absorber for reducing vibration of two-DOF vibration system. The conventional fixed-points method and genetics algorithm (GA) optimization procedure are utilized in designing the optimal parameter of DVA. The frequency and damping ratio are optimized to determine the optimal absorber parameters. The simulation results show that GA optimization procedure is more effective in designing the double DVA in comparison to the fixed-points method. The experimental study is conducted to verify the numerical result.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.26 no.1
• /
• pp.74-81
• /
• 2017

A crane is typically used as a means to lift and load equipment or materials. A surface vessel uses a towed object for underwater activity. Such a mechanism for dropping and lifting of equipment is necessary, and is called an overboarding unit. The present study is focused on the overboarding unit used for a crane structure. This paper deals with new overboarding mechanism design and GA-based $MATLAB^{(R)}$ optimization. By using a quadrilateral link mechanism, it is possible to set the constraint function for optimizing the GA method. The optimization with $MATLAB^{(R)}$ is followed by the $SolidWorks^{(R)}$ simulation and verification. When applying the proposed mechanism, the operator is expected to have a big advantage in safety and efficiency of operations. Furthermore, the technology developed in this study will be helpful in similar circumstances and in the proposed mechanism.

• Proceedings of the KIEE Conference
• /
• 2007.04a
• /
• pp.207-209
• /
• 2007

In this paper, we introduce a genetic optimization of fuzzy set-fuzzy model using successive tuning method to carry out the model identification of complex and nonlinear systems. To identity we use genetic alrogithrt1 (GA) sand C-Means clustering. GA is used for determination the number of input, the seleced input variables, the number of membership function, and the conclusion inference type. Information Granules (IG) with the aid of C-Means clustering algorithm help determine the initial paramters of fuzzy model such as the initial apexes of the, membership functions in the premise part and the initial values of polyminial functions in the consequence part of the fuzzy rules. The overall design arises as a hybrid structural and parametric optimization. Genetic algorithms and C-Means clustering are used to generate the structurally as well as parametrically optimized fuzzy model. To identify the structure and estimate parameters of the fuzzy model we introduce the successive tuning method with variant generation-based evolution by means of GA. Numerical example is included to evaluate the performance of the proposed model.

• Journal of Electrical Engineering and Technology
• /
• v.5 no.4
• /
• pp.545-551
• /
• 2010

A new family of DC to AC converters, referred to as multilevel inverter, has received much attention from industries and researchers for its high power and voltage applications. One of the conventional techniques for implementing the switching algorithm in these inverters is optimized harmonic stepped waveform (OHSW). However, the major problem in using this technique is eliminating low order harmonics by solving the nonlinear and complex equations. In this paper, a new approach called the "bacterial foraging algorithm" (BFA) is employed. This algorithm eliminates and optimizes the harmonics in a multilevel inverter. This method has higher speed, precision, and convergence power compared with the genetic algorithm (GA), a famous evolutionary algorithm. The proposed technique can be expanded in any number of levels. The purpose of optimization is to remove some low order harmonics, as well as to ensure the fundamental harmonic retained at the desired value. As a case study, a 13-level inverter is chosen. The comparison results by MATLAB software between the two optimization methods (BFA and GA) have shown the effectiveness and superiority of BFA over GA where convergence is desired to achieve global optimum.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.2 no.1
• /
• pp.95-110
• /
• 1998

Evolutionary Algorithms (EAs) are population-based optimization methods based on the principle of Darwinian natural selection. The representative methodology in EAs is genetic algorithm (GA) proposed by J. H. Holland, and the theoretical foundations of GA are the Schema Theorem and the Building Block Hypothesis. In the meaning of these foundational concepts, simple genetic algorithm (SGA) allocate more trials to the schemata whose average fitness remains above average. Although SGA does well in many applications as an optimization method, still it does not guarantee the convergence of a global optimum in GA-hard problems and deceptive problems. Therefore as an alternative scheme, there is a growing interest in a co-evolutionary system, where two populations constantly interact and co-evolve in contrast with traditional single population evolutionary algorithm. In this paper we show why the co-evolutionary algorithm works better than SGA in terms of an extended schema theorem. And predator-prey co-evolution and symbiotic co-evolution, typical approaching methods to co-evolution, are reviewed, and dynamic fitness landscape associated with co-evolution is explained. And the experimental results show a co-evolutionary algorithm works well in optimization problems even though in deceptive functions.

• Journal of Microbiology and Biotechnology
• /
• v.22 no.7
• /
• pp.939-946
• /
• 2012

Antibiotic production with Streptomyces sindenensis MTCC 8122 was optimized under submerged fermentation conditions by artificial neural network (ANN) coupled with genetic algorithm (GA) and Nelder-Mead downhill simplex (NMDS). Feed forward back-propagation ANN was trained to establish the mathematical relationship among the medium components and length of incubation period for achieving maximum antibiotic yield. The optimization strategy involved growing the culture with varying concentrations of various medium components for different incubation periods. Under non-optimized condition, antibiotic production was found to be $95{\mu}g/ml$, which nearly doubled ($176{\mu}g/ml$) with the ANN-GA optimization. ANN-NMDS optimization was found to be more efficacious, and maximum antibiotic production ($197{\mu}g/ml$) was obtained by cultivating the cells with (g/l) fructose 2.7602, $MgSO_4$ 1.2369, $(NH_4)_2PO_4$ 0.2742, DL-threonine 3.069%, and soyabean meal 1.952%, for 9.8531 days of incubation, which was roughly 12% higher than the yield obtained by ANN coupled with GA under the same conditions.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2010.06a
• /
• pp.337-337
• /
• 2010

갈륨-질화물 (GaN) 기반의 고 전자 이동도 트랜지스터 (High Electron Mobility Transistor, HEMT)는 GaN의 큰 밴드갭 (3.4~6.2 eV), 높은 항복전계 (Ec~3 MV/cm) 및 높은 전자 포화 속도 (saturation velocity $-107\;cm{\cdot}s-1$) 특성과 AlGaN/GaN 등과 같은 이종접합구조(Heterostructure )로부터 발생하는 높은 면밀도(Sheet Concentration)를 갖는 이차원 전자가스(Two-Dimensional Electron Gas, 2DEG) 채널로 인해 차세대 고출력/고전압 소자로서 각광받고 있다. 하지만 드레인 쪽의 게이트 에지부분에 집중되는 전계로 인한 애벌린치 할복현상(Breakdown)이 발생하는 문제점이 있다. 따라서 AlGaN/GaN HEMT의 항복전압 향상을 위한 방법으로 필드플레이트(Field-Plate) 구조가 많이 사용되고 있다. 본 논문에서는 2D 시뮬레이션을 통한 AlGaN/GaN HEMT의 필드플레이트 구조 최적화를 수행하였다. 이를 위해 ATLASTM 전산모사 프로그램을 이용하여 필드플레이트 길이, 절연체 증류 및 두께에 따른 전류 전압 특성 및 전계 분산효과에 대한 전산모사를 수행하여 그 결과를 비교, 분석 하였다, 이를 바탕으로 기존의 구조에 비해 약 300%이상 향상된 항복전압을 갖는 AlGaN/GaN HEMT의 최적화된 필드 플레이트 구조를 제안하였다.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2008.04a
• /
• pp.416-421
• /
• 2008

A Metropolis genetic algorithm (MGA) is a newly-developed hybrid algorithm combining simple genetic algorithm (SGA) and simulated annealing (SA). In the algorithm, favorable features of Metropolis criterion of SA are incorporated in the reproduction operations of SGA. This way, MGA alleviates the disadvantages of finding imprecise solution in SGA and time-consuming computation in SA. It has been successfully applied and the efficiency has been verified for the practical structural design optimization. However, applicability of MGA for the wider range of problems should be rigorously proved through the solution of mathematical optimization problems. Thus, performances of MGA for the typical mathematical problems are investigated and compared with those of conventional algorithms such as SGA, micro genetic algorithm (${\mu}GA$), and SA. And, for better application of MGA, the effects of acceptance level are also presented. From numerical Study, it is again verified that MGA is more efficient and robust than SA, SGA and ${\mu}GA$ in the solution of mathematical optimization problems having various features.

• Journal of Microbiology and Biotechnology
• /
• v.16 no.6
• /
• pp.863-869
• /
• 2006

The behavior of submerged membrane bioreactor (SMBR) filtration systems utilizing rapid air backpulsing as a cleaning technique to remove reversible foulants was investigated using a genetic algorithm (GA). A customized genetic algorithm with suitable genetic operators was used to generate optimal time profiles. From experiments utilizing short and long periods of forward and reverse filtration, various experimental process parameters were determined. The GA indicated that the optimal values for the net flux fell between 263-270 LMH when the forward filtration time ($t_f$) was 30-37 s and the backward filtration time ($t_b$) was 0.19-0.27 s. The experimental data confirmed the optimal backpulse duration and frequency that maximized the net flux, which represented a four-fold improvement in 24-h backpulsing experiments compared with the absence of backpulsing. Consequently, the identification of a region of feasible parameters and nonlinear flux optimization were both successfully performed by the genetic algorithm, meaning the genetic algorithm-based optimization proved to be useful for solving SMBR flux optimization problems.

• Smart Structures and Systems
• /
• v.26 no.4
• /
• pp.533-544
• /
• 2020

Effective bridge maintenance reduces bridge operation costs and extends its service life. The possibility of storing bridge life-cycle data in a 3D parametric model of the bridge through Bridge Information Modeling (BrIM) provides new opportunities to enhance current practices of bridge maintenance management. This study develops a Decision Support System (DSS), namely BrDSS, which employs BrIM and an efficient optimization model for bridge maintenance planning. The BrIM model in BrDSS extracts basic data of elements required for the optimization process and visualizes the inspection data and the optimization results to the user to help in decision makings. In the optimization module of the DSS, the specifically formulated Genetic Algorithm (GA) eliminates the chances of producing infeasible solutions for faster convergence. The practicality of the presented DSS was explored by utilizing the DSS in the maintenance planning of a bridge under operation in the southwest of Iran.