• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.8
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• pp.1-7
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• 2005

This paper proposes a local improvement method that combines extended topological clean up and optimization-based smoothing for homogenizing triangular grid system. First extended topological clean up procedures are applied to improve the connectivities of grid elements. Then, local optimization-based smoothing is performed for maximizing the distortion metric that measures grid quality. Using the local improvement strategy, we implement the grid homogenizations for two triangular grid examples. It is shown that the suggested algorithm improves the quality of the triangular grids to a great degree in an efficient manner and also can be easily applied to the remeshing algorithm in adaptive mesh refinement technique.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.100-106
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• 2017

In this paper, we propose a parallel optimization method of Aho-Corasick (AC) algorithm and Parallel Failureless Aho-Corasick (PFAC) algorithm using Open Computing Language (OpenCL) on Field Programmable Gate Array (FPGA). The low throughput of string matching engine causes the performance degradation of network process. Recently, many researchers have studied the string matching engine using parallel computing. FPGA's vendors offer a parallel computing platform using OpenCL. In this paper, we apply the AC and PFAC algorithm on DE1-SoC board with Cyclone V FPGA, where the optimization that considers FPGA architecture is performed. Experiments are performed considering global id, local id, local memory, and loop unrolling optimizations using PFAC algorithm. The performance improvement using loop unrolling is 129 times greater than AC algorithm that not adopt loop unrolling. The performance improvements using loop unrolling are 1.1, 0.2, and 1.5 times greater than those using global id, local id, and local memory optimizations mentioned above.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1998.04a
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• pp.273-279
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• 1998

An immune system has powerful abilities such as memory, recognition and learning to respond to invading antigens, and is applied to many engineering algorithm recently. In this paper, the combined optimization algorithm is proposed for multi-optimization problem by introducing the capability of the immune system that controls the proliferation of clones to the genetic algorithm. The optimizing ability of the proposed optimization algorithm is identified by using two multi-peak functions which have many local optimums and optimization of the unbalance response function for rotor model.

• Journal of applied mathematics & informatics
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• v.15 no.1_2
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• pp.313-321
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• 2004

In this paper we study the problem of three-dimensional layout optimization on the simplified rotating vessel of satellite. The layout optimization model with behavioral constraints is established and some effective and convenient conditions of performance optimization are presented. Moreover, we prove that the performance objective function is locally Lipschitz continuous and the results on the relations between the local optimal solution and the global optimal solution are derived.

• Journal of Communications and Networks
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• v.7 no.4
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• pp.471-477
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• 2005

This paper proposes an efficient constraint-based optimization model for the design of 3G mobile networks, such as universal mobile telecommunications system (UMTS). The model concerns about finding a set of sites for locating radio network controllers (RNCs) from a set of pre-defined candidate sites, and at the same time optimally assigning node Bs to the selected RNCs. All these choices must satisfy a set of constraints and optimize an objective function. This problem is NP-hard and consequently cannot be practically solved by exact methods for real size networks. Thus, this paper proposes a hybrid search strategy for tackling this complex and combinatorial optimization problem. The proposed hybrid search strategy is composed of three phases: A constraint satisfaction method with an embedded problem-specific goal which guides the search for a good initial solution, an optimization phase using local search algorithms, such as tabu algorithm, and a post­optimization phase to improve solutions from the second phase by using a constraint optimization procedure. Computational results show that the proposed search strategy and the model are highly efficient. Optimal solutions are always obtained for small or medium sized problems. For large sized problems, the final results are on average within $5.77\%$ to $7.48\%$ of the lower bounds.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.12
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• pp.1132-1139
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• 2008

The aim of this study is to apply shape optimization technique for the repair of aging airframe components, which may extend fatigue life substantially. Free-form optimum shapes of a cracked part to be reworked or replaced are investigated with the objective to minimize the peak local stress concentration or fatigue-damage. Iterative non-gradient method, which is based on an analogy with biological growth, is employed by incorporating the robust optimization method to take account of the stochastic nature of the loading conditions. Numerical examples of optimal hole shape in a flat plate are presented to validate the proposed method. The method is then applied to determine the reworked or replacement shape for the repair of a cracked rib in the rear assembly wing body of aircraft.

• The KIPS Transactions:PartB
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• v.14B no.5
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• pp.377-382
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• 2007

This paper proposes a training algorithm for global optimization of the parameters of radial basis function networks. Since conventional training algorithms usually perform only local optimization, the performance of the network is limited and the final network significantly depends on the initial network parameters. The proposed hybrid simulated annealing algorithm performs global optimization of the network parameters by combining global search capability of simulated annealing and local optimization capability of gradient-based algorithms. Via experiments for function approximation problems, we demonstrate that the proposed algorithm can find networks showing better training and test performance and reduce effects of the initial network parameters on the final results.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.1000-1013
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• 2017

Clustering is a NP-hard problem that is used to find the relationship between patterns in a given set of patterns. It is an unsupervised technique that is applied to obtain the optimal cluster centers, especially in partitioned based clustering algorithms. On the other hand, cat swarm optimization (CSO) is a new meta-heuristic algorithm that has been applied to solve various optimization problems and it provides better results in comparison to other similar types of algorithms. However, this algorithm suffers from diversity and local optima problems. To overcome these problems, we are proposing an improved version of the CSO algorithm by using opposition-based learning and the Cauchy mutation operator. We applied the opposition-based learning method to enhance the diversity of the CSO algorithm and we used the Cauchy mutation operator to prevent the CSO algorithm from trapping in local optima. The performance of our proposed algorithm was tested with several artificial and real datasets and compared with existing methods like K-means, particle swarm optimization, and CSO. The experimental results show the applicability of our proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.902-907
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• 1993

An efficient dynamic control algorithm that outperforms existing local torque optimization techniques for redundant manipulators is presented. The method resolves redundancy at the acceleration level. In this method, a systematic switching technique as a trade-off means between local torque optimization and global stability is proposed based on the stability condition proposed by Maciejewski [1]. Comparative simulations on a three-link planar arm show the effectiveness of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.183-186
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• 1995

A method is presented for optimization of Universal Learning Networks (ULN), where, together with gradient method, Simulated Annealing (SA) is employed to elude local minima. The effectiveness of the method is shown by its application to control of a crane system.