• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.596-604
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• 2020

Previous studies on pipe auto-routing algorithms generally used such algorithms as A*, Dijkstra, Genetic Algorithm, Particle Swarm Optimization, and Ant Colony Optimization, to satisfy the relevant constraints of its own field and improve the output quality. On the other hand, this study aimed to significantly improve path-finding speed by applying the Jump Point Search (JPS) algorithm, which requires lower search cost than the abovementioned algorithms, for pipe routing. The existing JPS, however, is limited to two-dimensional spaces and can only find the shortest path. Thus, it requires several improvements to be applied to pipe routing. Pipe routing is performed in a three-dimensional space, and the path of piping must be parallel to the axis to minimize its interference with other facilities. In addition, the number of elbows must be reduced to the maximum from an economic perspective, and preferred spaces in the path must also be included. The existing JPS was improved for the pipe routing problem such that it can consider the above-mentioned problem. The fast path-finding speed of the proposed algorithm was verified by comparing it with the conventional A* algorithm in terms of resolution.

• IE interfaces
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• v.23 no.2
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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.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1624-1627
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• 1997

The 6-DOF parallel manipulator is a closed-kindmatic chain robot manipulator that is capable of providing high structural rigidity and positional accuracy. Because of its advantage, the parallel manipulator have been widely used in many engineering applications such as vehicle/flight driving simulators, rogot maniplators, attachment tool of machining centers, etc. However, the kinematic analysis for the implementation of a real-time controller has some problem because of the lack of an efficient lagorithm for solving its highly nonliner forward kinematic equation, which provides the translational and orientational attitudes of the moveable upper platform from the lenght of manipulator linkages. Generally, Newton-Raphson method has been widely sued to solve the forward kinematic problem but the effectiveness of this methodology depend on how to set initial values. This paper proposes a hybrid method using genetic algorithm(GA) and Newton-Raphson method to solve forward kinematics. That is, the initial values of forward kinematics solution are determined by adopting genetic algorithm which can search grobally optimal solutions. Since determining this values, the determined values are used in Newton-Raphson method for real time calcuation.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.11
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• pp.1239-1244
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• 2015

In this paper, a new method for finding optimal and short span in Convolutional Self-Doubly Orthogonal(CDO) codes are proposed. This new algorithm based on Parallel Implicitly-Exhaustive search, where we applied dynamic search space reduction methods in order to reduce computational time for finding Optimal Span for R=1/2 rate CDO codes. The simulation results shows that speedup and error correction performance of the new algorithm is better.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.439-442
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• 2003

In order to solve the difficulties of parameter settings in SA algorithm, an improved practical SA algorithm is proposed by employing the threading techniques, appropriate software structures, and dynamic adjustments of temperature parameters. Threads provide a mechanism to realize a parallel processing under a disperse environment by controlling the flux of internal information of an application. Thread services divide a process by multiple processes leading to parallel processing of information to access common data. Therefore, efficient search is achieved by multiple search processes, different initial conditions, and automatic temperature adjustments. The proposed are methods are evaluated, for three types of Traveling Salesman Problem (TSP) (random-tour, fractal-tour, and TSPLIB test data)are used for the performance evaluation. The experimental results show that the computational time is 5％ decreased comparing to conventional SA algorithm, furthermore there is no need for manual parameter settings. These results also demonstrate that the proposed method is applicable to real-world vehicle routing problems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.2
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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.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.303-304
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• 2008

For the state estimation problem, the weighted least squares (WLS) method and the fast decoupled method are widely used at present. However, these algorithms can converge to local optimal solutions. Recently, modern heuristic optimization methods such as Particle Swarm Optimization (PSO) have been introduced to overcome the disadvantage of the classical optimization problem. However, heuristic optimization methods based on populations require a lengthy computing time to find an optimal solution. In this paper, we used PSO to search for the optimal solution of state estimation in power systems. To overcome the shortcoming of heuristic optimization methods, we proposed parallel processing of the PSO algorithm based on the PC cluster system. the proposed approach was tested with the IEEE-118 bus systems. From the simulation results, we found that the parallel PSO based on the PC cluster system can be applicable for power system state estimation.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.137-140
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• 1996

This paper proposes a unit commitment scheduling method based on Parallel Genetic Algorithm(PGA). Due to a variety of constraints to be satisfied, such as the minimum up and down time constraints, the search space of the UC problem is highly nonconvex. So, we used transputer which is one of the practical parallel processors. It can give us fastness and effectiveness features of the proposed method for solving the problem. To show the effectiveness of the PGA based unit commitment scheduling, we tested results for system of 5 units and we can get desirable results.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.04a
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• pp.136-145
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• 1993

This paper considers a reliability problem as a special type of flow problem and presents an algorithm to evaluate the exact 2-terminal reliability of networks by using a backtracking technique. It employs a polygon-to-chain reduction in addition to series and parallel reduction techniques to reduce execution time. In comparisons, it presents a much better performance than other algorithms known to us. We also propose a methodology to apply the algorithm for approximation of the system reliability.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.8
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• pp.1-9
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• 1998

A new parallel test algorithm and a built-in self test (BIST) architecture are developed to test various types of functional faults efficiently in content addressable memories (CAMs). In test mode, the read oepratin is replaced by one parallel content addressable search operation and the writing operating is performed parallely with small peripheral circuit modificatins. The results whow that an efficient and practical testing with very low complexity and area overhead can be achieved.