• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.245-248
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• 2000

This paper proposes a cooperative parallel tabu search which incorporates with the historical information exchange among processors in addition to its own searching of each processor. We investigate the influence of our proposed cooperative parallel tabu search by comparison with a serial tabu search. We also propose two extensions of the cooperative parallel tabu search which are the cooperative construction of tabu memory and the selection of cooperative partner. Through computational experiment, we observe the improvement of solutions by our proposed method.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.4
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• pp.477-483
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• 2015

The k-Winners-Take-All (kWTA) is an operation to find the largest k (>1) inputs among N inputs. Parallel search algorithm of kWTA for digital inputs is not invented yet, so most of digital kWTA architectures have O(N) time complexity. A parallel search algorithm for digital kWTA operation and the circuits for its VLSI implementation are presented in this paper. The proposed kWTA architecture can compare all inputs simultaneously in parallel. The time complexity of the new architecture is O(logN), so that it is scalable to a large number of digital data. The high-speed kWTA operation and its O(logN) dependency of the new architecture are verified by simulations. It takes 290 ns in searching for 5 winners among 1024 of 32 bit data, which is more than thousands of times faster than existing digital kWTA circuits, as well as existing analog kWTA circuits.

• International Journal of Contents
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• v.1 no.1
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• pp.50-58
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• 2005

Generally, multidimensional data such as image and spatial data require large amount of storage space. There is a limit to store and manage those large amounts of data in single workstation. If we manage the data on parallel computing environment which is being actively researched these days, we can get highly improved performance. In this paper, we propose a parallel multidimensional index structure that exploits the parallelism of the parallel computing environment. The proposed index structure is nP(processor)-nxmD(disk) architecture which is the hybrid type of nP-nD and 1P-nD. Its node structure in-creases fan-out and reduces the height of an index. Also, a range search algorithm that maximizes I/O parallelism is devised, and it is applied to k-nearest neighbor queries. Through various experiments, it is shown that the proposed method outperforms other parallel index structures.

• Journal of Sensor Science and Technology
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• v.24 no.3
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• pp.188-193
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• 2015

This paper presents a new byte-wise BCH (4122, 4096, 2) decoder, which treats byte-wise parallel operations so as to enhance its throughput. In particular, we evaluate the parallel processing technique for the most time-consuming components such as syndrome generator and Chien search owing to the iterative operations. Even though a syndrome generator is based on the conventional LFSR architecture, it allows eight consecutive bit inputs in parallel and it treats them in a cycle. Thus, it can reduce the number of cycles that are needed. In addition, a Chien search eliminates the redundant operations to reduce the hardware complexity. The proposed BCH decoder is implemented with VHDL and it is verified using a Xilinx FPGA. From the simulation results, the proposed BCH decoder can enhance the throughput as 43% and it can reduce the hardware complexity as 67% compared to its counterpart employing parallel processing architecture.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.327-329
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• 2001

This paper presents the application of Parallel genetic algorithm and parallel tabu search to search an optimal solution of a unit commitment problem. The proposed method previously searches the solution globally using the parallel genetic algorithm, and then searches the solution locally using tabu search which has the good local search characteristic to reduce the computation time. This method combines the benefit of both method, and thus improves the performance. To show the usefulness of the proposed method, we simulated for 10 units system. Numerical results show the improvements of cost and computation time compared to previous obtained results.

• IE interfaces
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• v.15 no.4
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• pp.338-348
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• 2002

In this paper, we present a restricted tabu search(RTS) algorithm that schedules jobs on identical parallel machines in order to minimize the maximum lateness of jobs. Jobs have release times and due dates. Also, sequence-dependent setup times exist between jobs. The RTS algorithm consists of two main parts. The first part is the MATCS(Modified Apparent Tardiness Cost with Setups) rule that provides an efficient initial schedule for the RTS. The second part is a search heuristic that employs a restricted neighborhood generation scheme with the elimination of non-efficient job moves in finding the best neighborhood schedule. The search heuristic reduces the tabu search effort greatly while obtaining the final schedules of good quality. The experimental results show that the proposed algorithm gives better solutions quickly than the existing heuristic algorithms such as the RHP(Rolling Horizon Procedure) heuristic, the basic tabu search, and simulated annealing.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.35
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• pp.107-114
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• 1995

This paper proposes the Tabu Search algorithm to minimize mean tardiness in the parallel machine scheduling problem. The algorithm reduces the computation time by employing restricted neighborhood and produces an efficient solution in this problem.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.6
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• pp.567-581
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• 2008

As the size of the web is growing explosively, search engines are becoming increasingly important as the primary means to retrieve information from the Internet. A search engine periodically downloads web pages and stores them in the database to provide readers with up-to-date search results. The web crawler is a program that downloads and stores web pages for this purpose. A large-scale search engines uses a parallel web crawler to retrieve the collection of web pages maximizing the download rate. However, the service architecture or experimental analysis of parallel web crawlers has not been fully discussed in the literature. In this paper, we propose an architecture of the parallel web crawler and discuss implementation issues in detail. The proposed parallel web crawler is based on the coordinator/agent model using multiple machines to download web pages in parallel. The coordinator/agent model consists of multiple agent machines to collect web pages and a single coordinator machine to manage them. The parallel web crawler consists of three components: a crawling module for collecting web pages, a converting module for transforming the web pages into a database-friendly format, a ranking module for rating web pages based on their relative importance. We explain each component of the parallel web crawler and implementation methods in detail. Finally, we conduct extensive experiments to analyze the effectiveness of the parallel web crawler. The experimental results clarify the merit of our architecture in that the proposed parallel web crawler is scalable to the number of web pages to crawl and the number of machines used.

• Journal of the Optical Society of Korea
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• v.19 no.1
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• pp.45-54
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• 2015

In this study, a novel parallel wavefront correction system architecture is proposed, and a model-based tabu search (MBTS) algorithm is introduced for this new system to compensate wavefront aberration caused by atmospheric turbulence in a free-space optical (FSO) communication system. The algorithm flowchart is presented, and a simple hypothetical design for the parallel correction system with multiple adaptive optical (AO) subsystems is given. The simulated performance of MBTS for an AO-FSO system is analyzed. The results indicate that the proposed algorithm offers better performance in wavefront aberration compensation, coupling efficiency, and convergence speed than a stochastic parallel gradient descent (SPGD) algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.220-224
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• 2008

This paper presents the polygon-based Q-leaning and Parallel SVM algorithm for object search with multiple robots. We organized an experimental environment with one hundred mobile robots, two hundred obstacles, and ten objects. Then we sent the robots to a hallway, where some obstacles were lying about, to search for a hidden object. In experiment, we used four different control methods: a random search, a fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process to determine the next action of the robots, and hexagon-based Q-learning, and dodecagon-based Q-learning and parallel SVM algorithm to enhance the fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process. In this paper, the result show that dodecagon-based Q-learning and parallel SVM algorithm is better than the other algorithm to tracking for object.