• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.2
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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.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07a
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• pp.916-918
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• 2005

다목적 최적화 문제의 목표는 다양한 파레토 최적해(Pareto Optimal Solution)을 찾는데 있으며, 마이크로-유전자 알고리즘(Micro-Genetic Algorithm)은 단순 유전자 알고리즘(Simple Genetic Algorithm)에 비해 소수의 유전자들만을 선별하여 진화시키는 방식으로 효율성을 극대화시킨다. 본 논문에서는 다양한 목적을 동시에 최적화하는 다목적 멀티캐스트 라우팅 문제를 해결하기 위해서 다목적 유전자 알고리즘과 마이크로-유전자 알고리즘을 결합한 다목적 마이크로-유전자 알고리즘을 적용하였다.

• Journal of Ship and Ocean Technology
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• v.9 no.4
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• pp.35-46
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• 2005

The performance of optimisation methods, based on penalty functions, is highly problem- dependent and many methods require additional tuning of some variables. This additional tuning is the influences of penalty coefficient, which depend strongly on the degree of constraint violation. Moreover, Binary-coded Genetic Algorithm (BGA) meets certain difficulties when dealing with continuous and/or discrete search spaces with large dimensions. With the above reasons, Real-coded Micro-Genetic Algorithm (R$\mu$GA) is proposed to find the global optimum of continuous and/or discrete nonlinear constrained engineering problems without handling any of penalty functions. R$\mu$GA can help in avoiding the premature convergence and search for global solution-spaces, because of its wide spread applicability, global perspective and inherent parallelism. The proposed R$\mu$GA approach has been demonstrated by solving three different engineering design problems. From the simulation results, it has been concluded that R$\mu$GA is an effective global optimisation tool for solving continuous and/or discrete nonlinear constrained real­world optimisation problems.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6A
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• pp.369-378
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• 2012

In this study, direct-search based optimization methods are applied for blade shape optimization of wind turbines and the optimization performances of several methods including conventional genetic algorithm, micro genetic algorithm and pattern search method are compared to propose a more efficient method. For this purpose, the currently available version of HARP_Opt (Horizontal Axis Rotor Performance Optimizer) code is enhanced to rationally evaluate the annual energy production value according to control strategies and to optimize the blade shape using pattern search method as well as genetic algorithm. The enhanced HARP_Opt code is applied to obtain the optimal turbine blade shape for 1MW class wind turbines. The results from pattern search method are compared with the results from conventional genetic algorithm and also micro genetic algorithm and it is found that the pattern search method has a better performance in achieving higher annual energy production and consistent optimal shapes and the micro genetic algorithm is better for reducing the calculation time.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.40-47
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• 1994

In this paper, Micro-Genetic algorithms($\mu$-GAs) is proposed on a minimum-time path planning for robot manipulator. which is a kind of optimization algorithm. The minimum-time path planning, which can allow the robot system to perform the demanded tasks with a minimum execution time, may be of consequence to improve the productivity. But most of the methods proposed till now suffers from a significant computation burden and can`t often find the optimaul values. One way to overcome such difficulties is to apply the Micro-Genetic Algorithms, which can allow to find the optimul values, to the minimum-time problem. This paper propose an approach for solving the minimum-time path planning by using Micro-Genetic Algorithms. The effectiveness of the proposed method is demonstrated using the 2 d.o.f plannar Robot manipulator.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.225-232
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• 2002

Simple genetic algorithm(SGA) has been used to optimize a lot of structural optimization problems because it can optimize non-linear problems and obtain the global solution. But, because of large evolving populations during many generations, it takes a long time to calculate fitness. Therefore this paper applied micro-genetic algorithm(μ -GA) to structural optimization and compared results of μ -GA with results of SGA. Additionally, the Paper applied μ -GA to gate optimization problem for injection molds by using simulation program CAPA.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2002.10a
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• pp.40-44
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• 2002

선박 설계시 최적화에 있어 종래에는 Random search Parametric study, Hook&Jeeves Method등이 사용되어져 왔으나 1960년대 Genetic algorithm이 소개되고 꾸준히 발전함과 함께 선박 설계에서도 Genetic algorithm이 사용되기 시작하였다. 본 논문에서는 이러한 Genetic algorithm 중 Simple Genetic algorithm(SGA), Micro Genetic algorithm(MGA), Threshold Genetic algorithm(TGA), Hybrid Genetic algorithm(HGA)을 선박 설계에 적용하여 그 성능을 비교 검토해 보았다. MGA는 계산 부담을 줄이기 위해 작은 개체로 효율적인 탐색을 하며, TGA는 local optimum에서 쉽게 벗어나게 할 수 있는 특징이 있다. HGA는 Hook&Jeeves Method를 Genetic algorithm과 병합되어 있다. 이를 바탕으로 본 논문에서 망간단괴 수송선의 경제성을 평가한다. 평가 방법은 연간 300만톤을 생산한다고 가정하여 연간 운송 용적을 동호제약으로 해서 최적화를 한 뒤, 이를 이용하여 몇가지 Case로 나누어서 초기 자본, 연간 비용, 20년간 총 비용을 계산하여 가장 경제적인 선박을 선택한다.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.44 no.1
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• pp.57-67
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• 2008

Recently, Korean shipbuilding industry is keeping up the position of world wide No. 1 in world shipbuilding market share. It is caused by endless efforts to develope new technologies and methods and fast development of IT technologies in Korea, to raise up its productivities and efficiency in shipbuilding industry with many kinds of optimizing methods including genetic algorithm or artificial life algorithm... etc. In this paper, we have suggested the artificial life algorithm with relay search micro genetic algorithm. and we have improved a defect of simple genetic algorithm for its slow convergence speed and added a variety of solution candidates with applying relay search simple genetic algorithm. Finally, we have developed intelligent agent system for ship CAE modeling. We have tried to offer some conveniences a ship engineer for repeated ship CAE modeling by changing ship design repeatedly and to increase its accuracy of a ship model with it.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.698-702
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• 1992

In this paper, Micro-Genetic algorithms(.mu.-GAs) is proposed on a minimum-time path planning for robot manipulator, which is a kind of optimization algorithm. The minimum-time path planning, which can allow the robot system to perform the demanded tasks with a minimum execution time, may be of consequence to improve the productivity. But most of the methods proposed till now suffers from a significant computation burden and can't often find the optimal values. One way to overcome such difficulties is to apply the Micro-Genetic Algorithms, which can allow to find the optimal values, to the minimum-time problem. This paper propose an approach for solving the minimum-time path planning by using Micro-Genetic Algorithms. The effectiveness of the proposed method is demonstrated using the 2 d.o.f plannar Robot manipulator.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.1
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• pp.79-87
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• 2017

The microphysical processes of the numerical weather prediction (NWP) model cover the following : fall speed, accretion, autoconversion, droplet size distribution, etc. However, the microphysical processes and parameters have a significant degree of uncertainty. Parameter estimation was generally used to reduce errors in NWP models associated with uncertainty. In this study, the micro- genetic algorithm and harmony search algorithm were used as an optimization algorithm for estimating parameters. And we estimate parameters of microphysics for the Unified model in the case of precipitation in Korea. The differences which occurred during the optimization process were due to different characteristics of the two algorithms. The micro-genetic algorithm converged to about 1.033 after 440 times. The harmony search algorithm converged to about 1.031 after 60 times. It shows that the harmony search algorithm estimated optimal parameters more quickly than the micro-genetic algorithm. Therefore, if you need to search for the optimal parameter within a faster time in the NWP model optimization problem with large calculation cost, the harmony search algorithm is more suitable.