• Proceedings of the KSME Conference
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• 2005.11a
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• pp.52.1-52.1
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• 2005

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.01a
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• pp.167-168
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• 2015

유전자 알고리즘은 문제 크기가 커짐에 따라 해집합이 폭발적으로 늘어나 최적해를 찾기 힘든 최적화 문제에 주로 적용되는 알고리즘으로, 최근에는 지리정보시스템(GIS)의 경로 최적화 문제, 게임에서의 길찾기, 인공지능에 많이 적용되고 있다. 마이크로 유전자 알고리즘은 일반 유전자 알고리즘에 비해 작은 크기의 모집단을 사용함으로써 알고리즘의 효율을 높일 수 있는 장점이 있다. 따라서, 본 논문에서는 무향 Rural Postman Problem 해법으로 마이크로 유전자 알고리즘의 적용 방법을 제안한다.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.1
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• pp.103-109
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• 2009

This paper presents structural design optimization of a micro milling machine for minimum weight and compliance using a genetic algorithm with dynamic penalty function. The optimization procedure consists of two design stages, which are the static and dynamic design optimization stages. The design problem, in this study, is to find out thickness of structural members which minimize the weight, the static compliance and the dynamic compliance of the micro milling machine under several constraints such as dimensional constraints, maximum compliance limit, and safety factor criterion. Optimization results showed a great reduction in the static and dynamic compliances at the spindle nose of the micro milling machine in spite of a little decrease in the machine weight.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.2
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• pp.101-107
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• 2005

In this paper, optimized operating parameters were found using multi-dimensional engine simulation software (KIVA-3V) and micro-genetic algorithm for heavy duty diesel engine. The engine operating condition considered was at 1,737 rev/min and 57 % load. Engine simulation model was validated using an engine equipped with a high pressure electronic unit injector (HEUI) system. Three important parameters were used for the optimization - boost pressure, EGR rate and start of injection timing. Numerical optimization identified HCCI-like combustion characteristics showing significant improvements for the soot and $NO_X$ emissions. The optimized soot and $NO_X$ emissions were reduced to 0.005 g/kW-hr and 1.33 g/kW-hr, respectively. Moreover, the optimum results met EPA 2007 mandates at the operating point considered.

• Journal of Korea Society of Industrial Information Systems
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• v.10 no.4
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• pp.54-64
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• 2005

The study investigated genetic algorithms for the optimal control model of maximum height vertical jumping. The model includes forward dynamic simulations by the neural excitation-control variables. Convergence of genetic algorithms is very slow. In this paper the micro genetic algorithm(micro-GA) was used to reduce the computation time. Then a near optimal solution from micro-GA was an initial solution for VF02, which is one of well-developed and proven nonlinear programming algorithms. This approach provided the successful optimal solution for maximum-height jumping without a reasonable initial guess.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.5
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• pp.489-495
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• 2014

A micro-genetic algorithm (MGA) is one of the improved forms of a genetic algorithm. It is used to reduce the number of iterations and the computing resources required by using small populations. The efficiency of MGAs has been proved through many problems, especially problems with 3-5 design variables. This study proposes an optimization algorithm based on the sequential design of experiments (SDOE) and an MGA. In a previous study, the authors used the SDOE technique to reduce trial-and-error in the conventional approximate optimization method by using the statistical design of experiments (DOE) and response surface method (RSM) systematically. The proposed algorithm has been applied to various mathematical examples and a structural problem.

• The KSFM Journal of Fluid Machinery
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• v.17 no.4
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• pp.19-29
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• 2014

In addition to the development of micro hydro turbine, the challenge in micro hydro turbine design as sustainable hydro devices is focused on the optimization of turbine runner blade which have decisive effect on the turbine performance to reach higher efficiency. A multi-objective optimization method to optimize the performance of runner blade of propeller turbine for micro turbine has been studied. For the initial design of planar blade cascade, singularity distribution method and the combination of the Bezier curve parametric technology is used. A non-dominated sorting genetic algorithm II(NSGA II) is developed based on the multi-objective optimization design method. The comparision with model test show that the blade charachteristics is optimized by NSGA-II has a good efficiency and load distribution. From model test and scale up calculation, the maximum prototype efficiency of the runner blade reaches as high as 90.87%.

• Journal of Navigation and Port Research
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• v.33 no.9
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• pp.635-644
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• 2009

The optimum design of power yacht belongs to the nonlinear constrained optimization problems. The determination of scantlings for the bow structure is a very important issue with in the whole structural design process. The derived design results are obtained by the use of real-coded micro-genetic algorithm including evaluation from Lloyd's Register small craft guideline, so that the nominal limiting stress requirement can be satisfied. In this study, the minimum volume design of bow structure on the power yacht was carried out based on the finite element analysis. The target model for optimum design and local structural analysis is the bow structure of a power yacht. The volume of bow structure and the main dimensions of structural members are chosen as an objective function and design variable, respectively. During optimization procedure, finite element analysis was performed to determine the constraint parameters at each iteration step of the optimization loop. optimization results were compared with a pre-existing design and it was possible to reduce approximately 19 percents of the total steel volume of bow structure from the previous design for the power yacht.

• Atmosphere
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• v.30 no.4
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• pp.335-346
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• 2020

Three free parameters included in a cumulus parameterization are optimized by using micro-genetic algorithm for three precipitation cases occurred in the Korea Peninsula during the summer season in order to reduce biases in a regional model associated with the uncertainties of the parameters and thus to improve the predictability of precipitation. The first parameter is the one that determines the threshold in convective trigger condition. The second parameter is the one that determines boundary layer forcing in convective closure. Finally, the third parameter is the one used in calculating conversion parameter determining the fraction of condensate converted to convective precipitation. Optimized parameters reduce the occurrence of convections by suppressing the trigger of convection. The reduced convection occurrence decreases light precipitation but increases heavy precipitation. The sensitivity experiments are conducted to examine the effects of the optimized parameters on the predictability of precipitation. The predictability of precipitation is the best when the three optimized parameters are applied to the parameterization at the same time. The first parameter most dominantly affects the predictability of precipitation. Short-range forecasts for July 2018 are also conducted to statistically assess the precipitation predictability. It is found that the predictability of precipitation is consistently improved with the optimized parameters.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.10a
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• pp.115-122
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• 2003

A Metropolis genetic algorithm(MGA) is developed and applied for the structural design optimization. In MGA favorable features of Metropolis algorithm in simulated annealing(SA) are incorporated in simple genetic algorithm(SGA), so that the MGA alleviates the disadvantage of finding imprecise solution in SGA and time-consuming computation in SA. Performances of MGA are compared with those of conventional algorithms such as Holland's SGA, Krishnakumar's micro genetic algorithm(μGA), and Kirkpatrick's SA. Typical numerical examples are used to evaluate the favorable features and applicability of MGA From the theoretical evaluation and numerical experience, it is concluded that the proposed MGA is a reliable and efficient tool for structural design optimization.