• 한국강구조학회 논문집
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• 제13권5호
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• pp.577-586
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• 2001

이 연구에서는 다 셀계수변수와 다 제약조건으로 구성된 단면 및 시스템 형상을 동시에 고려하는 입체 트러스의 이산화 최적설계 문제를 유전자 알고리즘을 이용하여 체계화하였다. 또한 유전자 알고리즘의 적용방법을 초기화절차 진화적 절차 그리고 유전적 절차로 구성하였다. 초기화 절차에서는 한 세대의 개체 수만큼 염색체를 생성하고 진화적 절차는 구조해석의 결과를 분석하여 적합도를 계산하였다. 그리고 유전적 절차는 복제와 교배 및 돌연변이를 통하여 다음 세대의 유전자를 생성하게 된다. 이렇게 진화적 절차와 유전적 절차를 반복 수행하여 최적 해를 탐색한다. 이 연구에서는 설계자가 궁극적 목표로 하는 구조물의 구조 해석과 단면 및 시스템 형상 최적설계를 동시에 수행할 수 있는 이산화 최적설계 프로그램을 개발하고 설계 예를 들어 비교 고찰하였다.

• 한국경영과학회지
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• 제37권4호
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• pp.67-72
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• 2012

In this paper a new method of handling linear constraints for the genetic algorithm is suggested. The method is designed to maintain the feasibility of offsprings during the evolution process of the genetic algorithm. In the genetic algorithm, the chromosomes are coded as the vectors in the real vector space constrained by the linear constraints. A method of handling the linear constraints already exists in which all the constraints of equalities are eliminated so that only the constraints of inequalities are considered in the process of the genetic algorithm. In this paper a new method is presented in which all the constraints of inequalities are eliminated so that only the constraints of equalities are considered. Several genetic operators such as arithmetic crossover, simplex crossover, simple crossover and random vector mutation are designed so that the resulting offspring vectors maintain the feasibility subject to the linear constraints in the framework of the new handling method.

• 제어로봇시스템학회논문지
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• 제8권12호
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• pp.1048-1055
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• 2002

The problem of generating torch paths for 2D laser cutting of a stock plate nested with a set of free-formed parts is investigated. The objective is to minimize the total length of the torch path starting from a blown depot, then visiting all the given Parts, and retuning back to the depot. A torch Path consists of the depot and Piercing Points each of which is to be specified for cutting a part. The torch path optimization problem is shown to be formulated as an extended version of the standard travelling salesman problem To solve the problem, a hybrid genetic algorithm is proposed. In order to improve the speed of evolution convergence, the algorithm employs a genetic algorithm for global search and a combination of an optimization technique and a genetic algorithm for local optimization. Traditional genetic operators developed for continuous optimization problems are used to effectively deal with the continuous nature of piercing point positions. Computational results are provided to illustrate the validity of the proposed algorithm.

• 한국경영과학회지
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• 제24권3호
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• pp.73-82
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• 1999

In this paper, we address a single machine non-preemptive n-job scheduling problem to minimize the sum of earliness and tardiness with different release times and due dates. To solve the problem, we propose a genetic algorithm with new crossover and mutation operators to find the job sequencing. For the proposed genetic algorithm, the optimal pair of crossover and mutation rates is investigated. To illustrate the suitability of genetic algorithm, solutions of genetic algorithm are compared with solutions of exhaustive enumeration method in small size problems and tabu search method in large size problems. Computational results demonstrate that the proposed genetic algorithm provides the near-optimal job sequencing in the real world problem.

• 산업경영시스템학회지
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• 제27권2호
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• pp.102-113
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• 2004

This paper addresses the transportation planning that is based on genetic algorithm for determining transportation time and transportation amount of minimizing cost of distribution system. The vehicle routing of minimizing the transportation distance of vehicle is determined. A distribution system is consisted of a distribution center and many retailers. The model is assumed that the time horizon is discrete and finite, and the demand of retailers is dynamic and deterministic. Products are transported from distribution center to retailers according to transportation planning. Cost factors are the transportation cost and the inventory cost, which transportation cost is proportional to transportation distance of vehicle when products are transported from distribution center to retailers, and inventory cost is proportional to inventory amounts of retailers. Transportation time to retailers is represented as a genetic string. The encoding of the solutions into binary strings is presented, as well as the genetic operators used by the algorithm. A mathematical model is developed. Genetic algorithm procedure is suggested, and a illustrative example is shown to explain the procedure.

• 산업기술연구
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• 제19권
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• pp.209-216
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• 1999

In this paper, we proposed a modofied genetic algorithm to minimize misclassification rate for determining the codebook. Genetic algorithms are adaptive methods which may be used solve search and optimization problems based on the genetic processes of biological organisms. But they generally require a large amount of computation efforts. GAVQ can choose the optimal individuals by genetic operators. The position of individuals are optimized to improve the recognition rate. The technical properties of this study is that prevents us from the local minimum problem, which is not avoidable by conventional VQ algorithms. We compared the simulation result with Matlab using phoneme data. The simulation results show that the recognition rate from GAVQ is improved by comparing the conventional VQ algorithms.

• 산업경영시스템학회지
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• 제26권4호
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• pp.23-30
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• 2003

This study develops an efficient heuristic which is based on genetic approach for AGVs flow path layout problem. The suggested solution approach uses a algorithm to replace two 0-1 integer programming models and a branch-and-bound search algorithm. Genetic algorithms are a class of heuristic and optimization techniques that imitate the natural selection and evolutionary process. The solution is to determine the flow direction of line in network AGVs. The encoding of the solutions into binary strings is presented, as well as the genetic operators used by the algorithm. Genetic algorithm procedure is suggested, and a simple illustrative example is shown to explain the procedure.

• 한국시뮬레이션학회논문지
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• 제20권3호
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• pp.79-88
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• 2011

함정 전투체계는 무기체계, 정보통신 등의 기술 발전으로 인한 복잡한 전장 환경에 적응하기 위하여 다양한 전술을 운용해야 한다. 현재 운용되는 국방 M&S 시스템은 의사결정을 위해 운용자, 즉 인간의 개입이 필수적이다. 하지만 인간이 개입되는 시뮬레이션은 실시간 정도의 저속에서 가능하고, 반복적인 실험의 어려움이 따르게 된다. 이를 개선하기 위해 에이전트 기반의 국방 M&S 시스템의 연구가 최근 들어 활발히 진행되고 있다. 그러나 현존하는 에이전트 기반 M&S 시스템은 고속 시뮬레이션은 가능하지만, 고정된 전술 분석용으로 활용되는데 그치고 있다. 따라서 본 논문에서는 주어진 시나리오에 대한 고속의 반복적인 실험 및 다양한 전술 운용과 창발적 전술 생성을 위해 진화론적 시뮬레이션을 이용한 다대다 함정교전 전술 생성 방법론을 제안하였다. 타당성 검증을 위해 서해상에서 벌어지는 가상의 3:3 함정교전 시뮬레이션을 수행하였고, 이를 통해 창발적 전술 생성의 가능성을 제시하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.215-219
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• 2003

In this paper, we generate a trajectory minimized the energy gait of a biped robot for walking a staircase using genetic algorithms and apply to the computed torque controller for the stable dynamic biped locomotion. In the saggital plane, a 6 degree of freedom biped robot that model consists of seven links is used. In order to minimize the total energy efficiency, the Real-Coded Genetic Algorithm (RCGA) is used. Operators of genetic algorithms are composed of a reproduction, crossover and mutation. In order to approximate the walking gait, the each joint angle is defined as a 4-th order polynomial of which coefficients are chromosomes. Constraints are divided into equality and inequality. Firstly, equality constraints consist of position conditions at the end of stride period and each joint angle and angular velocity condition for periodic walking. On the other hand, inequality constraints include the knee joint conditions, the zero moment point conditions for the x-direction and the tip conditions of swing leg during the period of a stride for walking a staircase.

• 대한산업공학회지
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• 제25권3호
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• pp.360-368
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• 1999

We consider the problem of scheduling n jobs with sequence-dependent processing times on a set of parallel-identical machines. The processing time of each job consists of a pure processing time and a sequence-dependent setup time. The objective is to maximize the total remaining machine available time which can be used for other tasks. For the problem, a hybrid genetic algorithm is proposed. The algorithm combines a genetic algorithm for global search and a heuristic for local optimization to improve the speed of evolution convergence. The genetic operators are developed such that parallel machines can be handled in an efficient and effective way. For local optimization, the adjacent pairwise interchange method is used. The proposed hybrid genetic algorithm is compared with two heuristics, the nearest setup time method and the maximum penalty method. Computational results for a series of randomly generated problems demonstrate that the proposed algorithm outperforms the two heuristics.