• The Journal of the Korea Contents Association
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• v.10 no.9
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• pp.107-116
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• 2010

The facility location problem is one of the traditional optimization problems. In this paper, we deal with the single source capacitated facility location problem (SSCFLP) and it is known as an NP-hard problem. Thus, it seems to be natural to use a heuristic approach such as evolutionary algorithms for solving the SSCFLP. This paper introduces a new efficient evolutionary algorithm for the SSCFLP. The proposed algorithm is devised by incorporating a general adaptive link adjustment evolutionary algorithm and three heuristic local search methods. Finally we compare the proposed algorithm with the previous algorithms and show the proposed algorithm finds optimum solutions at almost all middle size test instances and very stable solutions at larger size test instances.

• Journal of Internet Computing and Services
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• v.12 no.3
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• pp.89-99
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• 2011

The payload signature-based traffic classification system has to deal with large amount of traffic data, as the number of internet-based applications and network traffic continue to grow. While a number of pattern-matching algorithms have been proposed to improve processing speedin the literature, the performance of pattern matching algorithms is restrictive and depends on the features of its input data. In this paper, we studied how to optimize the search space in order to improve the processing speed of the payload signature-based traffic classification system. Also, the feasibility of our design choices was proved via experimental evaluation on our campus traffic trace.

• KIISE Transactions on Computing Practices
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• v.23 no.8
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• pp.492-497
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• 2017

A hypergraph consists of a set of nodes and hyperedges that connect an arbitrary number of nodes. We employ graph traversal algorithms such as BFS and DFS to analyze or explore hypergraph data. However, the conventional BFS and DFS do not consider the structural characteristics of hyperedges. In this paper, we propose a method to record visited edges and nodes during the traversal algorithm for data stored in hypergraphDB. In the experiments, we conduct various hypergraph analyses that utilize traversal algorithms and show that our method achieves a fewer number of database accesses and faster processing time than the conventional one.

• Smart Structures and Systems
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• v.13 no.3
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• pp.389-406
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• 2014

Optimal sensor placement techniques play a significant role in enhancing the quality of modal data during the vibration based health monitoring of civil structures, where many degrees of freedom are available despite a limited number of sensors. The literature has shown a shift in the trends for solving such problems, from expansion or elimination approach to the employment of heuristic algorithms. Although these heuristic algorithms are capable of providing a global optimal solution, their greatest drawback is the requirement of high computational effort. Because a highly efficient optimisation method is crucial for better accuracy and wider use, this paper presents an improved simulated annealing (SA) algorithm to solve the sensor placement problem. The algorithm is developed based on the sensor locations' coordinate system to allow for the searching in additional dimensions and to increase SA's random search performance while minimising the computation efforts. The proposed method is tested on a numerical slab model that consists of two hundred sensor location candidates using three types of objective functions; the determinant of the Fisher information matrix (FIM), modal assurance criterion (MAC), and mean square error (MSE) of mode shapes. Detailed study on the effects of the sensor numbers and cooling factors on the performance of the algorithm are also investigated. The results indicate that the proposed method outperforms conventional SA and Genetic Algorithm (GA) in the search for optimal sensor placement.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.1
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• pp.77-91
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• 2007

In this paper, we present a symbiotic evolutionary algorithm for multi-objective optimization. The goal in multi-objective evolutionary algorithms (MOEAs) is to find a set of well-distributed solutions close to the true Pareto optimal solutions. Most of the existing MOEAs operate one population that consists of individuals representing the entire solution to the problem. The proposed algorithm has a two-leveled structure. The structure is intended to improve the capability of searching diverse and food solutions. At the lower level there exist several populations, each of which represents a partial solution to the entire problem, and at the upper level there is one population whose individuals represent the entire solutions to the problem. The parallel search with partial solutions at the lower level and the Integrated search with entire solutions at the upper level are carried out simultaneously. The performance of the proposed algorithm is compared with those of the existing algorithms in terms of convergence and diversity. The optimization problems with continuous variables and discrete variables are used as test-bed problems. The experimental results confirm the effectiveness of the proposed algorithm.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.8
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• pp.763-767
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• 2008

In this paper, to make a high performance and low power CORDIC architecture for 3D operations in mobile devices, we suggest two off-line vectoring algorithms named Angle Based Search (ABS) and Scaling Considered Search (SCS). The ABS algorithm represents a 3D vector with two angles and those angles are used as a condition for searching CORDIC rotation sequences. The SCS algorithm determines the best CORDIC rotation sequence in advance to eliminate extra scaling computation. Using the proposed algorithms, we can observe 50% of latency is reduced. Furthermore, we perform a simple analysis and discuss possible reduction of power consumption by applying voltage scaling method together with the proposed algorithm.

• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.123-131
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• 2015

Training neural networks is a complex task with great importance in the field of supervised learning. In the training process, a set of input-output patterns is repeated to an artificial neural network (ANN). From those patterns weights of all the interconnections between neurons are adjusted until the specified input yields the desired output. In this paper, a new hybrid algorithm is proposed for global optimization of connection weights in an ANN. Dynamic swarms are shown to converge rapidly during the initial stages of a global search, but around the global optimum, the search process becomes very slow. In contrast, the gradient descent method can achieve faster convergence speed around the global optimum, and at the same time, the convergence accuracy can be relatively high. Therefore, the proposed hybrid algorithm combines the dynamic particle swarm optimization (DPSO) algorithm with the backpropagation (BP) algorithm, also referred to as the DPSO-BP algorithm, to train the weights of an ANN. In this paper, we intend to show the superiority (time performance and quality of solution) of the proposed hybrid algorithm (DPSO-BP) over other more standard algorithms in neural network training. The algorithms are compared using two different datasets, and the results are simulated.

• Journal of Electrical Engineering and Technology
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• v.5 no.1
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• pp.70-78
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• 2010

Elitist nondominated sorting genetic algorithm (NSGA-II) is adopted and improved for multiobjective optimal reactive power flow (ORPF) problem. Multiobjective ORPF, formulated as a multiobjective mixed integer nonlinear optimization problem, minimizes real power loss and improves voltage profile of power grid by determining reactive power control variables. NSGA-II-based ORPF is tested on standard IEEE 30-bus test system and compared with four other state-of-the-art multiobjective evolutionary algorithms (MOEAs). Pareto front and outer solutions achieved by the five MOEAs are analyzed and compared. NSGA-II obtains the best control strategy for ORPF, but it suffers from the lower convergence speed at the early stage of the optimization. Several problem-specific local search strategies (LSSs) are incorporated into NSGA-II to promote algorithm's exploiting capability and then to speed up its convergence. This enhanced version of NSGA-II (ENSGA) is examined on IEEE 30 system. Experimental results show that the use of LSSs clearly improved the performance of NSGA-II. ENSGA shows the best search efficiency and is proved to be one of the efficient potential candidates in solving reactive power optimization in the real-time operation systems.

• Journal of Information Technology and Architecture
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• v.11 no.2
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• pp.225-233
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• 2014

In this paper an architecture for 3D audio core algorithm evaluation database system. Due to increase of 3D audio system through multimedia device, an evaluation system is required for evaluating the 3D core algorithms for developing 3D audio system. Conventional evaluation systems have some problems. Researchers have to learn usage of evaluation system, in addition it is inefficient to use and search audio sources because audio sources are not indexed in general. To solve these problems, we design the architecture of 3D audio core algorithm evaluation database system enabling to automatically evaluate core algorithms using database management system. Also we define XML metadata scheme for information of saved audio source in database. This approach allows improving efficiency of search audio source and use of audio database.

• Smart Structures and Systems
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• v.15 no.1
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• pp.191-207
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• 2015

Proper placement of sensors plays a key role in construction and implementation of an effective structural health monitoring (SHM) system. This paper proposes a novel methodology called the distributed monkey algorithm (DMA) for the optimum design of SHM system sensor arrays. Different from the existing algorithms, the dual-structure coding method is adopted for the representation of design variables and the single large population is partitioned into subsets and each subpopulation searches the space in different directions separately, leading to quicker convergence and higher searching capability. After the personal areas of all subpopulations have been finished, the initial optimal solutions in every subpopulation are extracted and reordered into a new subpopulation, and the harmony search algorithm (HSA) is incorporated to find the final optimal solution. A computational case of a high-rise building has been implemented to demonstrate the effectiveness of the proposed method. Investigations have clearly suggested that the proposed DMA is simple in concept, few in parameters, easy in implementation, and could generate sensor configurations superior to other conventional algorithms both in terms of generating optimal solutions as well as faster convergence.