• 경영과학
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• 제18권1호
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• pp.105-118
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• 2001

The common shortest path problem is to find the shortest route between two specified nodes in a transportation network with only one traffic mode. The public transportation network with multiple traffic mode is a more realistic representation of the transportation system in the real world, but it is difficult for the conventional shortest path algorithms to deal with. The genetic algorithm (GA) is applied to solve this problem. The objective function is to minimize the sum of total service time and total transfer time. The individual description, the coding rule and the genetic operators are proposed for this problem.

• 한국컴퓨터정보학회논문지
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• 제10권6호
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• pp.279-286
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• 2005

본 연구에서는 캐피탈시장에서의 고객신용예측을 위한 모형으로 여러 가지 인공신경망(Neural Network) 모형들을 유전자 알고리즘(Genetic Algorithm)을 이용하여 통합한 신용예측모형을 제안하였다. 10개의 학습된 인공신경망 모형들을 유전자알고리즘을 이용하여 종류별로 통합하여 MLP (Multi-Layered Perceptron), Linear, RBF(Radial Basis Function) 세 가지의 대표모델을 얻고 이를 다시 하나의 인공신경망 모델로 통합하였다. 이를 통합되기 이전의 각각의 인공신경망 모형들과 성능을 비교, 분석하여 본 연구에서 제안한 통합모형의 유효성과 통합방법의 타당성을 제시하였다.

• 대한교통학회지
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• 제18권3호
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• pp.77-86
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• 2000

기존의 최단경로 탐색모형은 단일 목적을 대상으로 한다. 그러나 실제로는 통행자가 단일 목적만을 기준으로 경로를 선택하는 경우는 드물며, 경로선택은 통행시간과 비용 등 다양한 목적을 종합적으로 고려해서 결정되어진다. 따라서 최단경로는 여러 가지 목적을 고려해야 한다. 이러한 경우에 이들 목적간의 상충적인 관계로 인해 여러 가지 목적을 모두 만족시키는 최적경로는 존재치 않으며, 통행자가 고려하는 목적들의 중요도에 따라 다양한 경로가 선택되어질 수 있다. 다목적의 최적경로는 여러 가지 목적들의 절충(Trade-Off)을 고려한 다수의 파레토 최적경로(Pareto Optimal Path)가 탐색되어져야 한다. 그러나 기존의 다중목적을 고려한 최적경로 탐색 알고리즘은 하나 또는 일부의 파레토 최적경로만을 탐색하며 따라서 다양한 경로를 제공하지 못한다. 본 논문은 두 개의 목적을 고려한 최적경로 탐색 모형을 개발하는 것이다. 파레토 최적경로들은 대체경로로 사용할 수도 있다. 본 연구에서는 다양한 파레토 최적경로를 탐색하기 위해 본 모형의 개발에 유전 알고리즘(Genetic Algorithm)을 적용하였다.

• 산업경영시스템학회지
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• 제40권4호
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• pp.211-220
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• 2017

The application of the theoretical model to real assembly lines has been one of the biggest challenges for researchers and industrial engineers. There should be some realistic approach to achieve the conflicting objectives on real systems. Therefore, in this paper, a model is developed to synchronize a real system (A discrete event simulation model) with a theoretical model (An optimization model). This synchronization will enable the realistic optimization of systems. A job assignment model of the assembly line is formulated for the evaluation of proposed realistic optimization to achieve multiple conflicting objectives. The objectives, fluctuation in cycle time, throughput, labor cost, energy cost, teamwork and deviation in the skill level of operators have been modeled mathematically. To solve the formulated mathematical model, a multi-objective simulation integrated hybrid genetic algorithm (MO-SHGA) is proposed. In MO-SHGA each individual in each population acts as an input scenario of simulation. Also, it is very difficult to assign weights to the objective function in the traditional multi-objective GA because of pareto fronts. Therefore, we have proposed a probabilistic based linearization and multi-objective to single objective conversion method at population evolution phase. The performance of MO-SHGA is evaluated with the standard multi-objective genetic algorithm (MO-GA) with both deterministic and stochastic data settings. A case study of the goalkeeping gloves assembly line is also presented as a numerical example which is solved using MO-SHGA and MO-GA. The proposed research is useful for the development of synchronized human based assembly lines for real time monitoring, optimization, and control.

• Journal of Electrical Engineering and Technology
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• 제8권3호
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• pp.490-498
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• 2013

This paper discusses the application of evolutionary multi-objective optimization algorithms namely Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Modified NSGA-II (MNSGA-II) for solving the Combined Economic Emission Dispatch (CEED) problem with valve-point loading. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a non-smooth optimization problem. IEEE 57-bus and IEEE 118-bus systems are taken to validate its effectiveness of NSGA-II and MNSGA-II. To compare the Pareto-front obtained using NSGA-II and MNSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Furthermore, three different performance metrics such as convergence, diversity and Inverted Generational Distance (IGD) are calculated for evaluating the closeness of obtained Pareto-fronts. Numerical results reveal that MNSGA-II algorithm performs better than NSGA-II algorithm to solve the CEED problem effectively.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 추계학술대회 논문집
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• pp.42-45
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• 2002

In this paper, multiphase optimization of machine Tool structure is presented. The final goal is to obtain 1) light weight, 2) statically and dynamically rigid. and 3) thermally stable structure. The entire optimization process is carried out in three phases. In the first phase, multiple static optimization problem with two objective functions is treated using Pareto genetic algorithm. where two objective functions are weight of the structure and static compliance. In the second phase, maximum receptance is minimized using simple genetic algorithm. And the last phase, thermal deflection to moving heat sources is analyzed using Predictor-Corrector Method. The method is applied to a high speed line center design which takes the shape of back-column structure.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 추계학술대회
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• pp.637-642
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• 2004

This paper presents a new shape design method for the multi-mode sensor that can detect selected multiple modes for the active vibration control of mechanical structures. The structure used for this study is an isotropic cantilever beam type with a PVDF(polyvinylidene fluoride) which is bonded onto the structure as a sensor. Characteristic behaviors of the sensor are related with the electrode shapes of PVDF. The shape optimization problem is solved by defining a new multi-objective function and using the genetic algorithm. Resulting electrode shape functions have good performances to detect the multiple vibration modes. The results of analytical simulations are compared with those of experiment works. The results agree well each other. Hence, the obtained experimental results give evidence for the validity of the presented theoretical analysis of the electrode shape design problem.

• 대한기계학회논문집A
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• 제30권6호
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• pp.698-704
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• 2006

This paper presents an electrode shape design method for the multi-mode sensors that could deteict the selected structural multiple modes. The structure used for this study is an isotropic cantilever beam type with a PVDF (polyvinylidene fluoride) which is bonded onto the structure as a sensor. The shape optimization problem is solved by using Genetic Algorithm (GA) with an appropriate objective function. The performance of analytical optimal shape sensor is compared with that of experimental work. The results show that the, obtained electrode shape sensors have good performance to detect the multiple vibration modes simultaneously.

• Industrial Engineering and Management Systems
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• 제10권4호
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• pp.247-254
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• 2011

In this paper, we study a single-machine scheduling problem with deteriorating processing time of jobs and multiple rate-modifying activities which reset deteriorated processing time to the original processing time. In this situation, the objective function is to minimize total completion time. First, we formulate an integer programming model. Since the model is difficult to solve as the size of real problem being very large, we design an improved genetic algorithm called adaptive genetic algorithm (AGA) with spontaneously adjusting crossover and mutation rate depending upon the status of current population. Finally, we conduct some computational experiments to evaluate the performance of AGA with the conventional GAs with various combinations of crossover and mutation rates.

• Journal of Communications and Networks
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• 제8권1호
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• pp.116-122
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• 2006

Most of the multimedia applications require strict quality of service (QoS) guarantee during the communication between a single source and multiple destinations. This gives rise to the need for an efficient QoS multicast routing strategy. Determination of such QoS-based optimal multicast routes basically leads to a multi-objective optimization problem, which is computationally intractable in polynomial time due to the uncertainty of resources in Internet. This paper describes a network model for researching the routing problem and proposes a new multicast tree selection algorithm based on genetic algorithms to simultaneously optimize multiple QoS parameters. The paper mainly presents a QoS multicast routing algorithm based on genetic algorithm (QMRGA). The QMRGA can also optimize the network resources such as bandwidth and delay, and can converge to the optimal or near-optimal solution within few iterations, even for the networks environment with uncertain parameters. The incremental rate of computational cost can close to polynomial and is less than exponential rate. The performance measures of the QMRGA are evaluated using simulations. The simulation results show that this approach has fast convergence speed and high reliability. It can meet the real-time requirement in multimedia communication networks.