• Proceedings of the KSME Conference
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• 2001.06c
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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.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.430-438
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• 2004

Among the optimization method, GA (genetic algorithm) is a very powerful searching method enough to compete with design sensitivity analysis method. GA is very easy to apply, since it dose not require any design sensitivity information. However, GA has been computationally not efficient due to huge repetitive computation. In this study, parallel computation is adopted to Improve computational efficiency, Paralleled GA is introduced on a clustered LINUX based personal computer system.

• Journal of Navigation and Port Research
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• v.28 no.6
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• pp.517-523
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• 2004

The container load planning is one of key factors for efficient operations of handling equipments at container ports. When the number of containers are large, finding a good solution using the conventional genetic algorithm is very time consuming. To obtain a good solution with considerably small effort, in this paper a pseudo-parallel genetic algorithm(PPGA) based on both the migration model and the ring topology is developed The performance of the PPGA is demonstrated through a test problem of determining the optimal loading sequence of the containers.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.314-320
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• 2005

In this paper, a methodology for estimating the parameters of a discrete-time system and designing a digital PID controller based on the estimated model and a genetic algorithm is presented. To deal with optimization problems occurring regarding parameter estimation and controller design, a pseudo parallel genetic algorithm (PPGA) is used. The parameters of a discrete-time system are estimated using both the model technique and a PPGA. The digital PID controller is described by the pulse transfer function and its parameters are tuned based on both the model reference technique and another PPGA. A set of experimental works on two processes are carried out to illustrate the performance of the proposed method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.4
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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.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.40-42
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• 2003

본 논문에서는 유전자 알고리즘(Genetic Algorithm: GA)의 새로운 병렬화 방법을 제안 하고 있다. 기존의 병렬 유전자 알고리즘(Parallel Genetic Algorithm: PGA)은 전체 개체집단을 부개체집단 (Subpopulation)으로 나누어 해의 가능 영역을 동시에 탐색하는 것이 일반적인 방법인데 반해. 본 논문에서 제안하는 병렬화 방법은 전체 해의 영역을 나누어 각각의 영역에서 독립된 개체집단들이 서로 다른 영역을 탐색하게 하는 방법이다. 이 방법은 두 가지 단계의 병렬 유전자 알고리즘으로 구성된다. 먼저 적응교배 연산자(Adaptive Crossover Operator: ACO)를 이용한 PGA를 통해 지역해에 인접한 범위들로 해의 영역을 나누고, 이렇게 나누어진 각각의 영역들에서 다시 병렬로 GA를 적용시켜 자세하게 탐색하는 방법이다. 첫 번째 수행되는 PGA 단계에서는 탐색 시간을 줄이고 두 번째 PGA 단계에서는 보다 자세한 탐색을 하기 위해 정밀도(Precision)의 조정을 유전자 알고리즘의 병렬화에 적용하였으며. 이를 통해 빠르고 자세한 탐색이 가능한 유전자 알고리즘의 병렬화 방법을 제안하고 있다.

• Journal of information and communication convergence engineering
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• v.8 no.1
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• pp.13-18
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• 2010

The composite stock cutting problem is defined as allocating rectangular and irregular patterns onto a large composite stock sheet of finite dimensions in such a way that the resulting scrap will be minimized. In this paper, we introduce a novel approach to hybrid optimization algorithm called MGA in MPI (Message Passing Interface) environments. The proposed MGA combines the benefit of rapid convergence property of Mean Field Annealing and the effective genetic operations. This paper also proposes the efficient data structures for pattern related information.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.3
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• pp.1-12
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• 1992

Parallel Genetic Algorithms (GAs) are developed to solve a single machine n-job scheduling problem which is to minimize the sum of absolute deviations of completion times from a common due date. (0, 1) binary scheme is employed to represent the n-job schedule. Two selection methods, best individual selection and simple selection are examined. The effect of crossover operator, due date adjustment mutation and due date adjustment reordering are discussed. The performance of the parallel genetic algorithm is illustrated with some example problems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.59
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• pp.11-20
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• 2000

The Job Shop Scheduling Problem(JSSP) is one of the most general and difficult of all traditional scheduling problems. The goal of this research is to develop an efficient scheduling method based on single genetic algorithm(SGA) and parallel genetic algorithm (PGA) to address JSSP. In this scheduling method, new genetic operator, generating method of initial population are developed and island model PGA are proposed. The scheduling method based on PGA are tested on standard benchmark JSSP. The results were compared with SGA and another GA-based scheduling method. The PGA search the better solution or improves average of solution in benchmark JSSP. Compared to traditional GA, the proposed approach yields significant improvement at a solution.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.9
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• pp.1636-1641
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• 2008

In this study, we introduce a new approach to Parallel Genetic Algorithms (PGA) which combines AHFCGA with crossover operator. As to crossover operators, we use three types of the crossover operators such as modified simple crossover(MSX), arithmetic crossover(AX), and Unimodal Normal Distribution Crossover(UNDX) for real coding. The AHFC model is given as an extended and adaptive version of HFC for parameter optimization. The migration topology of AHFC is composed of sub-populations(demes), the admission threshold levels, and admission buffer for the deme of each threshold level through succesive evolution process. In particular, UNDX is mean-centric crossover operator using multiple parents, and generates offsprings obeying a normal distribution around the center of parents. By using test functions having multimodality and/or epistasis, which are commonly used in the study of function parameter optimization, Experimental results show that AHFCGA can produce more preferable output performance result when compared to HFCGA and RCGA.