• 한국가스학회지
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• 제1권1호
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• pp.33-40
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• 1997

다극 및 다목적함수 최적화 문제를 해결하기 위해서 유전 알고리듬을 이용한 일반적인 최적화 도구인 APROGA II가 개발되었다. 우선 다극 최적화를 위해서는 다중선택집합탐색 알고리듬을 이용하였다. 두 번째로 다목적함수의 최적화를 위해서는 파레토 우성 토너먼트와 공유개념을 이용한 선택방법과 선택집합을 이용한 연속적인 세대교체법을 이용하여 새로운 알고리듬을 제안하였다. 이들 알고리듬을 이용하여 3개의 탐색엔진(APROGA 탐색엔진, 다극 탐색엔진 그리고 다목적함수 탐색엔진)을 가지고, 이진 및 이산 변수를 다룰 수 있는 APROGA II 시스템이 개발되었다. 그리고 여러 가지 검토함수들과 사례연구들을 적용시켜서 다극 탐색엔진의 성공적인 적용성을 확인하였다.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집C
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• pp.170-175
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• 2001

The design cycle associated with large engineering systems requires an initial decomposition of the complex system into design processes which are coupled through the transference of output data. Some of these design processes may be grouped into iterative subcycles. In analyzing or optimizing such a coupled system, it is essential to determine the best order of the processes within these subcycles to reduce design cycle time and cost. This is accomplished by decomposing large multidisciplinary problems into several multidisciplinary analysis subsystems (MDASS) and processing it in parallel. This paper proposes new strategy for parallel decomposition of multidisciplinary problems to improve design efficiency by using the multiple objective genetic algorithm (MOGA), and a sample test case is presented to show the effects of optimizing the sequence with MOGA.

• 대한기계학회논문집A
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• 제27권4호
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• pp.499-506
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• 2003

The design cycle associated with large engineering systems requires an initial decomposition of the complex system into design processes which are coupled through the transference of output data. Some of these design processes may be grouped into iterative subcycles. In analyzing or optimizing such a coupled system, it is essential to determine the best order of the processes within these subcycles to reduce design cycle time and cost. This is accomplished by decomposing large multidisciplinary problems into several sub design structure matrices (DSMs) and processing them in parallel This paper proposes a new method for parallel decomposition of multidisciplinary problems to improve design efficiency by using the multi-objective genetic algorithm and two sample test cases are presented to show the effect of the suggested decomposition method.

• 대한기계학회논문집A
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• 제24권7호
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• pp.1661-1672
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• 2000

An immune system has powerful abilities such as memory, recognition and learning how to respond to invading antigens, and has been applied to many engineering algorithms in recent year. In this pap er, the combined optimization algorithm (Immune- Genetic Algorithm: IGA) is proposed for multi-optimization problems by introducing the capability of the immune system that controls the proliferation of clones to the genetic algorithm. The optimizing ability of the proposed combined algorithm is identified by comparing the result of optimization with simple genetic algorithm for two dimensional multi-peak function which have many local optimums. Also the new combined algorithm is applied to minimize the total weight of the shaft and the transmitted forces at the bearings. The inner diameter oil the shaft and the bearing stiffness are chosen as the design variables. The dynamic characteristics are determined by applying the generalized FEM. The results show that the combined algorithm and reduce both the weight of the shaft and the transmitted forces at the bearing with dynamic conatriants.

• 대한조선학회지
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• 제31권1호
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• pp.34-38
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• 1994

본 연구에서는 Genetic Algorithm을 사용하여 상기의 문제를 해결하고자 한다. 특히 다목적 함수 최적화에는 한 번의 최적화 계산으로 Pareto최적해 집합이 동시에 구해지는 새로운 방법인 MOGA(Multicriteria Optimization by Genetic Algorithm)을 개발하였다. 먼저 Genetic Alorithm의 기본 특성에 대해 살펴보고, 다양한 종류의 문제를 통해 Genetic Algorithm의 유용 성을 검토하였다.

• 한국가스학회:학술대회논문집
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• 한국가스학회 2000년도 추계학술발표회 논문집
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• pp.174-179
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• 2000

• 한국공작기계학회논문집
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• 제16권5호
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• pp.205-210
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• 2007

The integration method of binary and real encoding in genetic algorithm is proposed to deal with design variables of various types in gear drive design. The method is applied to optimum design of multi-stage gear drive. Integer and Discrete type design variables represent the number of teeth and module, and continuous type design variables represent face width, helix angle and addendum modification factor etc. The proposed genetic algorithm is applied for the gear ratio optimization and the volume optimization(minimization) of multi-stage geared motor which is used in field. In result, the proposed design optimization method shows an effectiveness in optimum design process and the new design has a better results compared with the existing design.

• 산업공학
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• 제13권2호
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• pp.204-210
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• 2000

This paper deals with the problem of mixed-model sequencing on an assembly line. In this sequencing problem we want to minimize the risk of the conveyor stoppage and the total utility work. This paper applies genetic algorithm to solve the mixed-model sequencing problem which is formulated as an integer programming. The solution we get from this algorithm is compared with the solution of Tsai(1995)'s.

• 대한조선학회지
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• 제31권2호
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• pp.25-27
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• 1994

최적설계에 대한 두번의 특집을 통해 최적설계, 다목적함수, 민감도 해석, 재설계, 지식기반시스템, 유전적 알고리듬, 신경회로망 등 새로운 방법들이 많이 소개되었다. 이들을 이론적인 배경에 의해 분류하면, 최적화 이론과 인공지능의 두 가지로 크게 나누어 볼 수 있다. 하지만 한편으로는 이들을 '어떻게 하면 더 나은 설계를 할 수 있는가\ulcorner'하는 한가지 목표를 달성하기 위해 제안되고 있는 여러가지 도구들로도 이해될 수 있다. 이글에서는 이 다양한 방법들에 대한 관련성을 설계 방법론이라는 관점에서 설명을 시도하고자 한다.

• 한국경영과학회지
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• 제24권2호
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• pp.121-134
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• 1999

In this paper, I propose a hybrid genetic algorithm(HGAM) incorporating a greedy interchange local optimization procedure for the multiobjective vehicle scheduling problems with service due times where three conflicting objectives of the minimization of total vehicle travel time, total weighted tardiness, and fleet size are explicitly treated. The vehicle is allowed to visit a node exceeding its due time with a penalty, but within the latest allowable time. The HGAM applies a mixed farming and migration strategy in the evolution process. The strategy splits the population into sub-populations, all of them evolving independently, and applys a local optimization procedure periodically to some best entities in sub-populations which are then substituted by the newly improved solutions. A solution of the HCAM is represented by a diploid structure. The HGAM uses a molified PMX operator for crossover and new types of mutation operator. The performance of the HGAM is extensively evaluated using the Solomons test problems. The results show that the HGAM attains better solutions than the BC-saving algorithm, but with a much longer computation time.