• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.3
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• pp.125-136
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• 2009

The container packing problem Is one of the traditional optimization problems, which is very related to the knapsack problem and the bin packing problem. In this paper, we deal with the quadratic multiple container picking problem (QMCPP) and it Is known as a NP-hard problem. Thus, It seems to be natural to use a heuristic approach such as evolutionary algorithms for solving the QMCPP. Until now, only a few researchers have studied on this problem and some evolutionary algorithms have been proposed. This paper introduces a new efficient evolutionary algorithm for the QMCPP. The proposed algorithm is devised by improving the original network random key method, which is employed as an encoding method in evolutionary algorithms. And we also propose local search algorithms and incorporate them with the proposed evolutionary algorithm. Finally we compare the proposed algorithm with the previous algorithms and show the proposed algorithm finds the new best results in most of the benchmark instances.

• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.58-64
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• 2007

In pattern recognition field, there are many optimization problems having exponential search spaces. To solve of sequential search algorithms seeking sub-optimal solutions have been used. The algorithms have limitations of stopping at local optimums. Recently lots of researches attempt to solve the problems using genetic algorithms. This paper explains the huge search spaces of typical problems such as feature selection, classifier ensemble selection, neural network pruning, and clustering, and it reviews the genetic algorithms for solving them. Additionally we present several subjects worthy of noting as future researches.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06d
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• pp.219-221
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• 2012

The concave utility in the Network Utility Maximization (NUM) problem is only suitable for elastic flows. In networks with multiclass traffic, the utility can be concave, linear, step or sigmoidal. Hence, the basic NUM becomes a nonconvex optimization problem. The current approach utilizes the standard dual-based decomposition method. It does not converge in case of scarce resource. In this paper, we propose an algorithm that always converges to a local optimal solution to the nonconvex NUM after solving a series of convex approximation problems. Our techniques can be applied to any log-concave utilities.

• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.2
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• pp.118-127
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• 1991

In this paper, criteria and algorithms for the optimal service rate in a closed queueing network have been established. The objective is to minimize total cost. It is shown that system throughput is increasing concave over the service rate of a node and cycle time is increasing convex over the set of service times with a single calss of cubsomers. This enables developing an algorithm using a steepest descent method when the cost function for service rate is convex. The efficiency of the algorithm rests on the fact that the steepest descent direction is readily obtained at each iteration from the MVA algorithm. Several numerical examples are presented. The major application of this research is optimization of facility capacity in a manufacturing system.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.4
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• pp.17-35
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• 1997

This paper considers the efficient design of routing path and wavelength assignment in the single-hop WDM optical transport networks. The connection demands between node-pairs are given and a connection between a pair of nodes must be made by only one lightpath. It is assumed that no wavelength conversion is allowed and the physical topology of the network and the number of available wavelengths of a physical link are also given. This paper proposes the method to route a path and assign a wavelength to the path, which guarantees fairness to every node pair. In order to guarantee fairness and maximize connections, an optimization model is established, which successively maximizes minimum connection ratio to the demand of each node pair. A heuristic algorithm with polynomial iterations is developed for the problem since the problem is NP-hard. The heuristic algorithm is implemented and applied to the networks with real problem sizes. The results of the application are compared with GAMS/OSL.

• Journal of Communications and Networks
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• v.12 no.4
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• pp.375-381
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• 2010

A flooding attack, such as DoS or Worm, can be easily created or even downloaded from the Internet, thus, it is one of the main threats to servers on the Internet. This paper presents an online real-time network response system, which can determine whether a LAN is suffering from a flooding attack within a very short time unit. The detection engine of the system is based on the incremental mining of fuzzy association rules from network packets, in which membership functions of fuzzy variables are optimized by a genetic algorithm. The incremental mining approach makes the system suitable for detecting, and thus, responding to an attack in real-time. This system is evaluated by 47 flooding attacks, only one of which is missed, with no false positives occurring. The proposed online system belongs to anomaly detection, not misuse detection. Moreover, a mechanism for dynamic firewall updating is embedded in the proposed system for the function of eliminating suspicious connections when necessary.

• 전기의세계
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• v.38 no.2
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• pp.23-30
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• 1989

과학기술의 고속화에 따라 종래에는 인간이 더 잘수행하던 지능을 요하는 일들이 점점 컴퓨터에 의해 처리되는 경우가 늘어나고 있다. 그러나 아직도 많은 분야에서 종래의 방식에 의한 한개의 processor나 몇개의 간단한 multiprocessor시스템으로는 너무 많은 시간이 걸려서 처리할 수 없는 일들이 산재해 있다. 예를 들어 image를 인식하는 것이나 언어습득과 번역과 같은 문제들에서 인간의 뇌가 현존하고 있는 어떤 컴퓨터의 성능보다 능가하는 것을 알고 있다. 그 이유는 근본적으로 디지탈 컴퓨터와 인간의 뇌 사이의 정보 처리방식이 틀린점에 기인한다. 대부분의 컴퓨터는 한개 또는 몇개의 main procrssor들이 자료들을 sequential하게 처리하는 데에 반하여, 인간의 뇌는 수천억 개의 neuron들이 정보를 병렬로 처리하고 있다. 따라서 인간의 뇌에 대한 깊이 연구와 그에 따른 새로운 형태의 정보처리 기술의 개발이 시급히 요구되어지고 있다. 최근에 이와같은 massive parallelism을 구현하기 위한 시도가 neural network를 중심으로 일어나고 있다. 여기에서 사용되는 algorithm들은 local information을 실현한다. Neural network에는 여러가지 model들이 있으나 본고에서는 optimization model을 중심으로 그 배경 및 응용을 소개하고자 한다.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.75-79
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• 2000

This paper proposes the optimal placement with minimum set of Phasor Measurement Units (PMU's) using tabu search and makes an alternative plan to secure the robustness of the network with PMU's. The optimal PMU Placement (OPP) problem is generally expressed as a combinatorial optimization problem subjected to the observability constraints. Thus, it is necessary to make a use of an efficient method in solving the OPP problem. In this paper, a tabu search based approach to solve efficiently this OPP problem proposed. The observability of the network with PMU's is fragile at any single PMU contingency. To overcome the fragility, an alternative scheme that makes efficient use of the existing measurement system in power system state estimation proposed. The performance of the proposed approach and the alternative scheme is evaluated with IEEE sample systems.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.737-741
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• 1994

We show an application of a genetic algorithm to, control systems including neural networks. Genetic algorithms are getting more popular nowadays because of their simplicity and robustness. Genetic algorithms are global search techniques for optimization and many other problems. A feed-forward neural network which is widely used in control applications usually learns by error back propagation algorithm(EBP). But, when there exist certain constraints, EBP can not be applied. We apply a modified genetic algorithm to such a case. We show simulation examples of two cart-pole nonlinear systems: single pole and double pole.

• International Journal of Control, Automation, and Systems
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• v.4 no.1
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• pp.53-62
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• 2006

In this paper, ATM network congestion control with explicit rate feedback is considered. In ATM networks, delays commonly appear in data transmission and have to be considered in congestion control design. In this paper, a bounded single round delay on the return path is considered. Our objective is to design an explicit rate feedback control that achieves a robust optimal $H_2$ performance regardless of the bounded time-varying delays. An optimization approach in terms of linear matrix inequalities (LMIs) is given. Saturation in source rate and queue buffer is also taken into consideration in the proposed design. Simulations for the cases of single source and multiple sources are presented to demonstrate the effectiveness of the design.