• 한국공작기계학회논문집
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• 제12권3호
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• pp.82-88
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• 2003

SGA(Single Genetic Algorithm) is a heuristic global optimization method based on the natural characteristics and uses many populations and stochastic rules. Therefore SGA needs many function evaluations and takes much time for convergence. In order to solve the demerits of SGA, ${\mu}GA$(Micro-Genetic Algorithm) has recently been developed. In this study, ${\mu}GA$ which have small populations and fast convergence rate, was applied to structural optimization with discrete or integer variables such as 3, 10 and 25 bar trusses. The optimized results of ${\mu}GA$ were compared with those of SGA. Solutions of ${\mu}GA$ for structural optimization were very similar or superior to those of SGA, and faster convergence rate was obtained. From the results of examples, it is found that ${\mu}GA$ is a suitable and very efficient optimization algorithm for structural design.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2003년도 춘계학술대회 논문집
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• pp.9-14
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• 2003

SGA (Single Genetic Algorithm) is a heuristic global optimization method based on the natural characteristics and uses many populations and stochastic rules. Therefore SGA needs many function evaluations and takes much time for convergence. In order to solve the demerits of SGA, $\mu$GA(Micro-Genetic Algorithm) has recently been developed. In this study, $\mu$GA which have small populations and fast convergence rate, was applied to structural optimization with discrete or integer variables such as 3, 10 and 25 bar trusses. The optimized results of $\mu$GA were compared with those of SGA. Solutions of $\mu$GA for structural optimization were very similar or superior to those of SGA, and faster convergence rate was obtained. From the results of examples, it is found that $\mu$GA is a suitable and very efficient optimization algorithm for structural design.

• 대한기계학회논문집A
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• 제26권2호
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• pp.406-414
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• 2002

A new approach, referred to as a successive zooming genetic algorithm (SZGA), is Proposed for identifying a global solution for continuous optimization problems. In order to improve the local fine-tuning capability of GA, we introduced a new method whereby the search space is zoomed around the design point with the best fitness per 100 generation. Furthermore, the reliability of the optimized solution is determined based on the theory of probability. To demonstrate the superiority of the proposed algorithm, a simple genetic algorithm, micro genetic algorithm, and the proposed algorithm were tested as regards for the minimization of a multiminima function as well as simple functions. The results confirmed that the proposed SZGA significantly improved the ability of the algorithm to identify a precise global minimum. As an example of structural optimization, the SZGA was applied to the optimal location of support points for weight minimization in the radial gate of a dam structure. The proposed algorithm identified a more exact optimum value than the standard genetic algorithms.

• International Journal of Ocean System Engineering
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• 제2권4호
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• pp.233-238
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• 2012

In this study, we carried out a precedent investigation for an ultra-large container ship, which is expected to be a higher value-added vessel. We studied a preliminary optimized design technique for estimating the principal dimensions of an ultra-large container ship. Above all, we have developed optimized dimension estimation models to reduce the building costs and weight, using previous container ships in shipbuilding yards. We also applied a generalized estimation model to estimate the shipping service costs. A Genetic Algorithm, which utilized the RFR (required freight rate) of a container ship as a fitness value, was used in the optimization technique. We could handle uncertainties in the shipping service environment using a Monte-Carlo simulation. We used several processes to verify the estimated dimensions of an ultra-large container ship. We roughly determined the general arrangement of an ultra-large container ship up to 1500 TEU, the capacity check of loading containers, the weight estimation, and so on. Through these processes, we evaluated the possibility for the practical application of the preliminary design model.

• 한국농공학회논문집
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• 제47권3호
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• pp.3-13
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• 2005

The conceptual rainfall-runoff models are used to predict complex hydrological effects of a basin. However, to obtain reliable results, there are some difficulties and problems in choosing optimum model, calibrating, and verifying the chosen model suitable for hydrological characteristics of the basin. In this study, Genetic Algorithm and SCE-UA method as global optimization methods were applied to compare the each optimization technique and to analyze the application for the rainfall-runoff models. Modified TANK model that is used to calculate outflow for watershed management and reservoir operation etc. was optimized as a long term rainfall-runoff model. And storage-function model that is used to predict real-time flood using historical data was optimized as a short term rainfall-runoff model. The optimized models were applied to simulate runoff on Pyeongchang-river watershed and Bocheong-stream watershed in 2001 and 2002. In the historical data study, the Genetic Algorithm and the SCE-UA method showed consistently good results considering statistical values compared with observed data.

• 소성∙가공
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• 제28권4호
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• pp.203-211
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• 2019

In this study, the weight of type IV pressure vessel is optimized through the burst pressure condition using the finite element analysis (FEA) based on the genetic algorithm (GA). The optimization design variables include the thickness of composite layers and the winding angles. The optimized design variables are validated using the numerical simulations for the pressure vessel. Consequently, the weight is decreased by about 6.5% as compared to the previously reported results for Type III pressure vessel. Additionally, a method which reduces the entire optimization time is proposed. In the original method, the population size is constant across all generations. However, the proposed method could reduce the workload through the reduction of the population size by half for every 25 generations. Thus, the proposed method is observed to increase the weight by about 0.1%, however, the working time for the optimization could be decreased by about 46.5%.

• 지질공학
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• 제30권4호
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• pp.435-446
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• 2020

The use of groundwater in Korea has increased in recent years to the point where its extraction is restricted in times of drought. This work models the groundwater pumping field as a confined aquifer in a simplified simulation of groundwater flow. It proposes a genetic algorithm to maximize groundwater development using a conceptual model of a steady-state confined aquifer. Solving the groundwater flow equation numerically calculates the hydraulic head along the domain of the problem; the algorithm subsequently offers optimized pumping strategies. The algorithm proposed here is designed to improve a prior initial groundwater management model. The best solution is obtained after 200 iterations. The results compare the computing time for five simulation cases. This study shows that the proposed algorithm can facilitate better groundwater development compared with a basic genetic algorithm.

• 한국콘텐츠학회논문지
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• 제21권7호
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• pp.164-170
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• 2021

VLSI 설계에서의 분할(partitioning)은 기능의 최적화를 위하여 설계하고자 하는 회로의 그룹화(grouping)하는 단계로서 레이아웃(layout)에서 면적과 전파지연의 최소화를 위해 함께 배치할 소자를 결정하는 문제이다. 이러한 분할 문제에서 해를 얻기 위해 사용되는 알고리즘은 Kernighan-Lin 알고리즘, Fiduccia Mattheyses heuristic, 시뮬레이티드 어닐링, 유전자 알고리즘 등의 방식이 이용된다. 본 논문에서는 회로 분할 문제에 대하여 유전자 알고리즘과 확률 진화 알고리즘을 결합한 어댑티드 유전자 알고리즘을 이용한 해 공간 탐색(solution space search) 방식을 제안하였으며, 제안한 방식을 유전자 알고리즘 및 시뮬레이티드 어닐링 방식과 비교, 분석하였고, 어댑티드 유전자 알고리즘이 시뮬레이티드 어닐링 및 유전자 알고리즘보다 더 효과적으로 최적해에 근접하는 것을 알 수 있었다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.104-106
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• 2007

In this paper, we propose an approach to design of optimized Cascade controller for Rotary Inverted Pendulum system using Hierarchical Fair Competition-based Genetic Algorithm(HFCGA). GAs may get trapped in a sub-optimal region of the search space thus becoming unable to find better quality solutions, especially for very large search space. The Parallel Genetic Algorithms(PGA) are developed with the aid of global search and retard premature convergence. HFCGA is a kind of multi-populations of PGA. In this paper, we design optimized Cascade controller by HFCGA for Rotary Inverted Pendulum system that is nonlinear and unstable. Cascade controller comprise two feedback loop, parameters of controller optimize using HFCGA. Then designed controller evaluate by apply to the real plant.

• 조명전기설비학회논문지
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• 제24권10호
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• pp.149-156
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• 2010

In this paper, the optimal design of a wind generator, implemented with the hybridized GA(Genetic Algorithm) and ES(Expert System), has been performed to maximize the AEP(Annual Energy Production) over the whole wind speed characterized by the statistical model of wind speed distribution. In particular, to solve the problem of calculation iterate, ES finds the superior individual and apply to initial generation of GA and it makes reduction of search domain. Meanwhile, for effective searching in reduced search domain, it propose Intelligent GA algorithm. Also, it shows the results of optimized model 500[kW] wind generator using hybridized algorithm and benchmark result of compare with GA.