• 대한안전경영과학회지
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• 제13권3호
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• pp.107-114
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• 2011

Traveling salesman problem is to minimize the total cost for a traveling salesman who wants to make a tour given finite number of cities along with the cost of travel between each pair them, visiting each cities exactly once before returning home. Traveling salesman problem is known to be NP-hard, and it needs a lot of computing time to get the optimal solution, so that heuristics are more frequently developed than optimal algorithms. This study suggests a hybrid parallel genetic algorithm(HPGA) for traveling salesman problem The suggested algorithm combines parallel genetic algorithm, nearest neighbor search, and 2-opt. The suggested algorithm has been tested on 7 problems in TSPLIB and compared the results of existing methods(heuristics, meta-heuristics, hybrid, and parallel). Experimental results shows that HPGA could obtain good solution in total travel distance minimization.

• 대한수학회지
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• 제52권2호
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• pp.373-388
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• 2015

In this paper, a novel parallel hybrid iterative method is proposed for finding a common element of the set of solutions of a system of equilibrium problems, the set of solutions of variational inequalities for inverse strongly monotone mappings and the set of fixed points of a finite family of nonexpansive mappings in Hilbert space. Strong convergence theorem is proved for the sequence generated by the scheme. Finally, a parallel iterative algorithm for two finite families of variational inequalities and nonexpansive mappings is established.

• 한국자동차공학회논문집
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• 제9권5호
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• pp.105-114
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• 2001

In this study, a shift control algorithm far improving the shift quality of a parallel hybrid drivetrain with an automated manual transmission (AM) is proposed. The general AMT requires the sophisticated control of clutch in the clutch engagement to improve its shift characteristics, and that is generally known to be difficult. But in this hybrid drivetrain, we can control the speeds of clutch plates by engine and motor control, and it provides the easier clutch control in shift process than general AMT. Additionally, it permits the much-reduced shift shock. The motor control during the shift period is also to achieve reduced velocity drop of the vehicle in comparison with that of a general AMT. Furthermore various dynamometer-based experiments are carried out to prove the validity of the proposed shift control algorithm.

• 융합신호처리학회논문지
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• 제16권3호
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• pp.83-89
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• 2015

Connected component labeling (CCL) is a mandatory step in image segmentation where objects are extracted and uniquely labeled. CCL is a computationally expensive operation and thus is often done in parallel processing framework to reduce execution time. Various parallel CCL methods have been proposed in the literature. Among them are NSZ label equivalence (NSZ-LE) method, modified 8 directional label selection (M8DLS) method, HYBRID1 method, and HYBRID2 method. Soh et al. showed that HYBRID2 outperforms the others and is the best so far. In this paper we propose a new hybrid parallel CCL algorithm termed as HYBRID3 that combines selective four directional label search (S4DLS) with label backtracking (LB). We show that the average percentage speedup of the proposed over M8DLS is around 60% more than that of HYBRID2 over M8DLS for various kinds of images.

• 산업경영시스템학회지
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• 제33권2호
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• pp.48-55
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• 2010

Reliability has been considered as a one of the major design measures in various industrial and military systems. The main objective is to suggest a mathematical programming model and a hybrid parallel genetic algorithm(HPGA) for the problem that determines the optimal component reliability to maximize the system reliability under cost constraint in this study. Reliability optimization problem has been known as a NP-hard problem and normally formulated as a mixed binary integer programming model. Component structure, reliability, and cost were computed by using HPGA and compared with the results of existing meta-heuristic such as Ant Colony Optimization(ACO), Simulated Annealing(SA), Tabu Search(TS) and Reoptimization Procedure. The global optimal solutions of each problem are obtained by using CPLEX 11.1. The results of suggested algorithm give the same or better solutions than existing algorithms, because the suggested algorithm could paratactically evolved by operating several sub-populations and improving solution through swap and 2-opt processes.

• Journal of applied mathematics & informatics
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• 제4권1호
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• pp.1-16
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• 1997

In this paper we study efficient parallel implementation for hybrid iterative methods BICGSTAB and BICGSTAB $(\ell)$ with ${Well}=2$ on the CRAY C90 and the efficiency of their parallel performance is evaluated. numerical experiments suggest that on the CRAY C90 a parallel inner product algorithm called PDOTB be used for the par-allelization of hybrid iterative methods containing sensitive values of inner products. Lastly it is shown that the number of iterations in which parallel hybrid iterative methods satisfy a certain convergence criterion depends on the number of processors to be used.

• 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.

• 산업공학
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• 제23권2호
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• pp.147-155
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• 2010

Reliability allocation is defined as a problem of determination of the reliability for subsystems and components to achieve target system reliability. The determination of both optimal component reliability and the number of component redundancy allowing mixed components to maximize the system reliability under resource constraints is called reliability-redundancy allocation problem(RAP). The main objective of this study is to suggest a mathematical programming model and a hybrid parallel genetic algorithm(HPGA) for reliability-redundancy allocation problem that decides both optimal component reliability and the number of component redundancy to maximize the system reliability under cost and weight constraints. The global optimal solutions of each example are obtained by using CPLEX 11.1. The component structure, reliability, cost, and weight were computed by using HPGA and compared the results of existing metaheuristic such as Genetic Algoritm(GA), Tabu Search(TS), Ant Colony Optimization(ACO), Immune Algorithm(IA) and also evaluated performance of HPGA. The result of suggested algorithm gives the same or better solutions when compared with existing algorithms, because the suggested algorithm could paratactically evolved by operating several sub-populations and improve solution through swap, 2-opt, and interchange processes. In order to calculate the improvement of reliability for existing studies and suggested algorithm, a maximum possible improvement(MPI) was applied in this study.

• Nuclear Engineering and Technology
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• 제50권3호
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• pp.327-339
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• 2018

This article presents a new global-local hybrid coarse-mesh finite difference (HCMFD) method for efficient parallel calculation of pin-by-pin heterogeneous core analysis. In the HCMFD method, the one-node coarse-mesh finite difference (CMFD) scheme is combined with a nodal expansion method (NEM)-based two-node CMFD method in a nonlinear way. In the global-local HCMFD algorithm, the global problem is a coarse-mesh eigenvalue problem, whereas the local problems are fixed source problems with boundary conditions of incoming partial current, and they can be solved in parallel. The global problem is formulated by one-node CMFD, in which two correction factors on an interface are introduced to preserve both the surface-average flux and the net current. Meanwhile, for accurate and efficient pin-wise core analysis, the local problem is solved by the conventional NEM-based two-node CMFD method. We investigated the numerical characteristics of the HCMFD method for a few benchmark problems and compared them with the conventional two-node NEM-based CMFD algorithm. In this study, the HCMFD algorithm was also parallelized with the OpenMP parallel interface, and its numerical performances were evaluated for several benchmarks.

• 한국자동차공학회논문집
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• 제7권5호
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• pp.96-106
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• 1999

In this study, the advanced shift control algorithm for parallel type hybrid drivetrain system with automated manual transmission(AMT) is proposed. The AMT can be easily realized by mounting the pneumatic actuators and sensors on the clutch and shift levers of the conventional manual transmission. By using the electronic-controlled AMT, engine and induction machine, it is possible to achieve the integrated control of overall system for the efficiency and the performance of the vehicle. Performing the speed control of the induction machine and the engine, the synchronization at gear shifting and the smooth engagement of clutch can be guaranteed. And it enables to reduce the shift shock and shorten the shift time. Hence, it results in the improvement of shift quality and the driving comfort of the vehicle. Dynamometer-based experiments are carried out to prove the validity of the proposed shift control algorithm.