• 대한기계학회논문집A
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• 제38권5호
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• pp.489-495
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• 2014

마이크로 유전알고리즘은 적은 수의 개체 사용 및 무작위 개체 구성을 통한 돌연변이 기능 대체의 특징을 갖는 진화연산을 수행하여 일반적인 유전알고리즘이 갖는 각 세대당 많은 계산 량이 요구되는 단점을 극복하고자 하였다. 이러한 마이크로 알고리즘은 특히 설계변수가 3~5 개를 갖는 문제에 효율적이라는 것이 많은 연구자들에 의하여 알려졌다. 따라서 본 연구의 목적은 순차적 실험계획법과 마이크로 유전알고리즘을 이용한 최적화 알고리즘을 개발하는 것이며, 이를 수학예제와 구조물 문제에 적용하여 실용성을 확인하고자 한다. 순차적 실험계획법은 저자들의 선행연구에서 제안되었으며, 실험계획법과 반응표면법을 이용하는 근사최적화 기법에 의한 시행착오적인 반복과정을 최소화하고자 하는 방법으로써, 행렬실험과 평균분석을 반복 적용하는 개념이다.

• 대한산업공학회지
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• 제37권3호
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• pp.163-170
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• 2011

This paper proposes a modified version of Particle Swarm Optimization (PSO) called Information Diffusion PSO (ID-PSO). In PSO algorithms, premature convergence of particles could be prevented by defining proper population topology. In this paper, we propose a variant of PSO algorithm using a new population topology. We draw inspiration from the theory of information diffusion which models the transmission of information or a rumor as one-to-one interactions between people. In ID-PSO, a particle interacts with only one particle at each iteration and they share their personal best solutions and recognized best solutions. Each particle recognizes the best solution that it has experienced or has learned from another particle as the recognized best. Computational experiments on the benchmark functions show the effectiveness of the proposed algorithm compared with the existing methods which use different population topologies.

• 대한기계학회논문집A
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• 제30권2호
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• pp.128-134
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• 2006

Topology optimization is an effective scheme to obtain the initial design concept: however, it is hard to apply in case of non-linear or multi-objective problems. In this study, a modified topology optimization method is proposed to generate a structure of a swing arm type actuator satisfying maximum compliance as well. as maximum stiffness using the multi-objective optimization. approach. The multi-objective function is defined to maximize the compliance in the direction of focusing of the actuator and the second eigen-frequency of the structure. The design of experiments are performed and the response surface functions are formulated to construct the multi-objective function. The weighting factors between conflicting functions are determined by the back-error propagation neural network and the solution of multi-objective function is acquired using the genetic algorithm.

• 대한기계학회논문집B
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• 제32권4호
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• pp.317-326
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• 2008

The main purpose of the present study is to perform shape optimizations of transonic compressor blade in order to enhance its performance. In this study, the Latin hypercube sampling of design of experiments and the weighted average surrogate model with the help of a gradient based optimization algorithm are used within design space by the lower and upper limits of each design variable and for finding optimum designs, respectively. 3-D Reynolds-averaged Navier-Stokes solver is used to evaluate the objective functions of adiabatic efficiency and pressure ratio. Six variables from lean and airfoil thickness profile are selected as design variables. The results show that the adiabatic efficiency is enhanced by 1.43% by efficiency optimization while the pressure ratio is increased very small, and pressure ratio is increased by 0.24% by pressure ratio optimization.

• 대한산업공학회지
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• 제29권1호
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• pp.90-99
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• 2003

Object clustering, which makes classification for a set of objects into a number of groups such that objects included in a group have similar characteristic and objects in different groups have dissimilar characteristic each other, has been exploited in diverse area such as information retrieval, data mining, group technology, etc. In this study, an object-clustering problem with similarity coefficients between objects is considered. At first, an evaluation function for the optimization problem is defined. Then, a genetic algorithm and local optimization technique based on heuristic method are proposed and used in order to obtain near optimal solutions. Solutions from the genetic algorithm are improved by local optimization techniques based on object relocation and cluster merging. Throughout extensive experiments, the validity and effectiveness of the proposed algorithms are tested.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권7호
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• pp.2800-2814
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• 2020

5G network optimization problem is a challenging optimization problem in the practical engineering applications. In this paper, to tackle this issue, Pareto fronts-driven Multi-Objective Cuckoo Search (PMOCS) is proposed based on Cuckoo Search. Firstly, the original global search manner is upgraded to a new form, which is aimed to strengthening the convergence. Then, the original local search manner is modified to highlight the diversity. To test the overall performance of PMOCS, PMOCS is test on three test suits against several classical comparison methods. Experimental results demonstrate that PMOCS exhibits outstanding performance. Further experiments on the 5G network optimization problem indicates that PMOCS is promising compared with other methods.

• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 2003년도 춘계학술발표대회 개요집
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• pp.172-175
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• 2003

A genetic algorithm was applied to an arc welding process to determine near optimal settings of welding process parameters which produce good weld quality. It has an advantage of being able to find optimal conditions with a fewer number of experiments than conventional full factorial design. According to the conventional full factorial design, in order to find the optimal welding conditions, 16,384 experiments must be performed. The genetic algorithm however, found the near optimal welding conditions from less than 60 experiments.

• 품질경영학회지
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• 제47권4호
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• pp.701-711
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• 2019

Purpose: The problem of optimizing redundancy allocation in multi-level systems is considered when each item in a multi-level system has alternative items with the same function. The number of redundancy of multi-level system is allocated to maximize the reliability of the system under path set and cost limitation constraints. Methods: Based on cost limitation and path set constraints, a mathematical model is established to maximize system reliability. Particle swarm optimization is employed for redundant allocation and verified by numerical experiments. Results: Comparing the particle swarm optimization method and the memetic algorithm for the 3 and 4 level systems, the particle swarm optimization method showed better performance for solution quality and search time. Particularly, the particle swarm optimization showed much less than the memetic algorithm for variation of results. Conclusion: The proposed particle swarm optimization considerably shortens the time to search for a feasible solution in MRAP with path set constraints. PS optimization is expected to reduce search time and propose the better solution for various problems related to MRAP.

• 공업화학
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• 제32권6호
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• pp.621-626
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• 2021

MOF-801 합성공정의 최적화를 위해 순차적인 실험 계획법을 이용하였다. 먼저 screening을 위한 완전 2-요인 설계와 이후 반응표면 분석법 중에 하나인 중심합성 계획법을 연속적으로 사용하였다. 두 가지 반응변수인 MOF-801의 결정화도와 BET 비표면적 중에 실험계획법에 보다 적합한 변수를 선택하기 위하여 fumaric acid, dimethylformamide (DMF) 및 formic acid의 몰비를 이용한 2³ 요인 설계법을 수행하였다. MINITAB 19 소프트웨어에 따라 설계된 8번의 MOF-801 합성 실험을 수행한 이후 XRD 분석 및 질소흡착법을 이용하여 특성분석을 수행하였다. 두 가지 반응변수 중 결정화도의 R²이 0.999로 BET 비표면적보다 실험계획법에 보다 적합하였다. 분산 분석(ANOVA)을 통해 fumaric acid와 formic acid의 몰 비가 MOF-801의 결정화도를 결정하는 주요 인자임을 확인하였다. response optimization과 두 인자의 contour plot을 통해 최적의 몰비는 ZrOCl₂·8H₂O : fumaric acid : DMF : formic acid = 1 : 1: 39 : 35로 추정되었다. 이후 합성반응 공정의 최적화를 위해 도출된 전구체의 몰 비 조건에서 합성 기간과 온도에 대한 박스-벤켄설계법을 수행하였다. 설계된 9번의 합성실험을 통해 도출된 결과를 2차 모델 방정식을 이용하여 계산하였다. 이를 이용하여 MOF-801의 최대 결정화도는 합성시간 7.8 h 그리고 합성온도 123 ℃의 조건에서 얻을 수 있음을 예측하였다.

• 한국자동차공학회논문집
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• 제13권1호
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• pp.119-125
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• 2005

The current automotive is seeking the improvement of performance, the prevention of environmental pollution and the saving of energy resources according to miniaturization and lightweight of the components. And the variance analysis on the basis of structure analysis and DOE is applied to the lower control am. We have proposed a statistical design model to evaluate the effect of structural modification by performing the practical multi-objective optimization considering weight, stress and fatigue lift. The lower control arm is performed the fatigue analysis using the load history of real road test. The design model is determined using the optimization of acquired load history with the fatigue characteristic. The characteristic function is made use of the optimization according to fatigue characteristics to consider constrained function in the optimization of DOE. The structure optimization of a lower control arm according to fatigue characteristics is performed. And the optimized design variable is D=47 m, T=36mm, W=12 mm. In the real engineering problem of considering many objective functions, the multi-objective optimization process using the mathematical programming and the characteristic function is derived an useful design solution.