• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권2호
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• pp.522-541
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• 2016

Network virtualization provides an effective way to overcome the Internet ossification problem. As one of the main challenges in network virtualization, virtual network embedding refers to mapping multiple virtual networks onto a shared substrate network. However, existing heuristic embedding algorithms evaluate the embedding potential of the nodes simply by the product of different resource attributes, which would result in an unbalanced embedding. Furthermore, ignoring the hops of substrate paths that the virtual links would be mapped onto may restrict the ability of the substrate network to accept additional virtual network requests, and lead to low utilization rate of resource. In this paper, we introduce and extend five node attributes that quantify the embedding potential of the nodes from both the local and global views, and adopt the technique for order preference by similarity ideal solution (TOPSIS) to rank the nodes, aiming at balancing different node attributes to increase the utilization rate of resource. Moreover, we propose a novel two-stage virtual network embedding algorithm, which maps the virtual nodes onto the substrate nodes according to the node ranks, and adopts a shortest path-based algorithm to map the virtual links. Simulation results show that the new algorithm significantly increases the long-term average revenue, the long-term revenue to cost ratio and the acceptance ratio.

• 산업공학
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• 제22권2호
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• pp.153-164
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• 2009

An effective management for reverse flows of products such as reuse, repair and disposal, has become an important issue for every aspect of business. In this paper, we study the Location-Routing Problem (LRP) in the multi-stage closed-loop supply chain network. The closed-loop supply chain in this study integrated both forward and reverse flows. In forward flow, a factory, Distribution Center (DC) and retailer are considered as usual. Additionally in reverse flow, we consider the Central Returns collection Center (CRC) and disposal facility. We propose a mixed integer programming model for the design of closed-loop supply chain integrating both forward and reverse flows. Since the LRP belongs to an NP-hard problem, we suggest a heuristic algorithm based on genetic algorithm. For some test problems, we found the optimal locations and routes by changing the numbers of retailers and facility candidates. Furthermore, we compare the efficiencies between open-loop and closed-loop supply chain networks. The results show that the closed-loop design is better than the open one in respect to the total routing distance and cost. This phenomenon enlarges the cut down effect on cost as an experimental space become larger.

• 한국ITS학회 논문지
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• 제8권1호
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• pp.82-91
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• 2009

기지국과 이동 단말 간의 경로 설정, 즉 라우팅은 멀티홉 셀룰러 시스템의 핵심 기술 중 하나이다. 또한, 멀티홉 셀룰러 시스템에서 기지국과 중계기들이 각 셀의 자원을 공유하므로, 전체 시스템의 가용 무선 자원을 최대한 이용할 수 있는 자원 할당 기법이 필요하다. 본 논문에서는 OFDMA 기반 멀티홉 셀룰러 시스템을 위한 통합 자원할당-라우팅 기법을 제안한다. 제안하는 기법은 전체 시스템의 하향 링크 처리율을 최대화하기 위한 통합 자원 할당-라우팅 문제를 MMKP 기반 휴리스틱 알고리즘을 이용하여 근사 해를 구한다. 실험 결과는 제안하는 기법이 시스템의 하향 링크 처리율 측면에서 링크 품질 기반 라우팅 기법보다 높은 성능을 나타내며, 최적 해를 도출하는 기법에 근접한 성능을 나타냄을 보인다.

• 한국경영과학회지
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• 제41권2호
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• pp.53-65
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• 2016

Static wireless multi-hop networks, such as wireless mesh networks and wireless sensor networks have proliferated in recent years because of they are easy to deploy and have low installation cost. Two key measures are used to evaluate the performance of a multicast tree algorithm or protocol : end-to-end delay and the number of transmissions. End-to-end delay is the most important measure in terms of QoS because it affects the total throughput in wireless networks. Delay is similar to the hop count or path length from the source to each destination and is directly related to packet success ratio. In wireless networks, each node uses the air medium to transmit data, and thus, bandwidth consumption is related to the number of transmission nodes. A network has many transmitting nodes, which will cause many collisions and queues because of congestion. In this paper, we optimize two metrics through a guaranteed delay scheme. We provide an integer linear programming formulation to minimize the number of transmissions with a guaranteed hop count and preprocessing to solve the aforementioned problem. We extend this scheme not only with the guaranteed minimum hop count, but also with one or more guaranteed delay bounds to compromise two key metrics. We also provide an explanation of the proposed heuristic algorithm and show its performance and results.

• 한국ITS학회 논문지
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• 제17권6호
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• pp.211-223
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• 2018

릴레이 시스템은 커버리지 확장과 셀 경계(Cell-Edge)의 시스템 처리량 향상을 목적으로 4세대 이동통신 시스템에 적용되어 왔다. 릴레이 시스템은 커버리지 확장과 시스템 처리량 증대에 효과적이지만 기존 단일 홉 시스템과 달리 추가 자원을 사용하기 때문에 릴레이 시스템에 특화된 경로선택 및 무선자원 할당 알고리즘의 적용을 요구한다. 본 논문에서는 협력 통신을 이용하는 LTE-Advanced 릴레이 시스템을 위한 통합 경로선택 및 자원할당 기법을 제안한다. 제안하는 기법은 라그랑지 승수 기반의 휴리스틱 알고리즘으로, 다중차원 다중선택 배낭 문제(Multi-dimensional Multi-choice Knapsack Problem)의 형태로 정의된 협력 통신 기반의 LTE-Advanced 릴레이 시스템 하향링크 처리율 최대화 문제의 근사 해를 구한다. 제안된 기법에 의해 도출된 근사 해의 성능이 최적 해의 성능에 충분히 근접할 수 있음을 시뮬레이션을 통해 보인다.

• Journal of Electrical Engineering and Technology
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• 제5권2호
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• pp.179-190
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• 2010

This paper describes a new stochastic heuristic algorithm in engineering problem optimization especially in power system applications. An improved particle swarm optimization (PSO) called adaptive particle swarm optimization (APSO), mixed with simulated annealing (SA), is introduced and referred to as APSO-SA. This algorithm uses a novel PSO algorithm (APSO) to increase the convergence rate and incorporate the ability of SA to avoid being trapped in a local optimum. The APSO-SA algorithm efficiency is verified using some benchmark functions. This paper presents the application of APSO-SA to find the optimal location, type and size of flexible AC transmission system devices. Two types of FACTS devices, the thyristor controlled series capacitor (TCSC) and the static VAR compensator (SVC), are considered. The main objectives of the presented method are increasing the voltage stability index and over load factor, decreasing the cost of investment and total real power losses in the power system. In this regard, two cases are considered: single-type devices (same type of FACTS devices) and multi-type devices (combination of TCSC, SVC). Using the proposed method, the locations, type and sizes of FACTS devices are obtained to reach the optimal objective function. The APSO-SA is used to solve the above non.linear programming optimization problem for better accuracy and fast convergence and its results are compared with results of conventional PSO. The presented method expands the search space, improves performance and accelerates to the speed convergence, in comparison with the conventional PSO algorithm. The optimization results are compared with the standard PSO method. This comparison confirms the efficiency and validity of the proposed method. The proposed approach is examined and tested on IEEE 14 bus systems by MATLAB software. Numerical results demonstrate that the APSO-SA is fast and has a much lower computational cost.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2014년도 추계학술대회
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• pp.342-345
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• 2014

기계학습을 이용한 네트워크 침입탐지시스템은 선택된 특징 조합에 따라 정확성 및 효율성 측면에서 크게 영향을 받는다. 하지만 일반적으로 사용되는 침입탐지용 특징들로부터 최적의 조합을 찾아내는 일은 많은 계산량을 요구한다. 예를 들어 n개로 구성된 특징들로부터 가능한 특징조합은 $2^n-1$ 개이다. 본 논문에서는 이러한 문제를 해결하기 위한 최적 특징 선택 알고리즘을 제시한다. 제안한 알고리즘은 최적화 문제 해결을 위한 대표적인 메타 휴리스틱 알고리즘인 지역탐색 알고리즘에 기반 한다. 또한 특징 조합을 평가를 위해 선택된 특징 요소와 k-means 군집화 알고리즘을 이용해 구해진 군집화의 정확성을 비용함수로 사용한다. 제안한 특징 선택 알고리즘의 평가를 위해 NSL-KDD 데이터와 인공 신경망을 사용해 특징 모두를 사용한 경우와 비교한다.

• 경영과학
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• 제14권2호
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• pp.47-61
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• 1997

This paper considers the parallel machines scheduling problem characterized as a multi-objective combinatorial problem. As this problem belongs to the NP-complete problem, genetic algorithms are applied instead of the traditional analytical approach. The purpose of this study is to show how the problem can be effectively solved by using genetic algorithms with a permutation approach. First, a permutation representation which can effectively represent the chromosome is introduced for this problem . Next, a schedule builder which employs the combination of scheduling theories and a simple heuristic approach is suggested. Finally, through the computer experiments of genetic algorithm to test problems, we show that the niche formation method does not contribute to getting better solutions and that the PMX crossover operator is the best among the selected four recombination operators at least for our problem in terms of both the performance of the solution and the operational convenience.

• 한국경영과학회지
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• 제14권2호
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• pp.47-47
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• 1989

This paper considers the parallel machines scheduling problem characterized as a multi-objective combinatorial problem. As this problem belongs to the NP-complete problem, genetic algorithms are applied instead of the traditional analytical approach. The purpose of this study is to show how the problem can be effectively solved by using genetic algorithms with a permutation approach. First, a permutation representation which can effectively represent the chromosome is introduced for this problem . Next, a schedule builder which employs the combination of scheduling theories and a simple heuristic approach is suggested. Finally, through the computer experiments of genetic algorithm to test problems, we show that the niche formation method does not contribute to getting better solutions and that the PMX crossover operator is the best among the selected four recombination operators at least for our problem in terms of both the performance of the solution and the operational convenience.

• 산업경영시스템학회지
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• 제39권3호
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• pp.83-89
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• 2016

The group formation problem of the machine and part is a critical issue in the planning stage of cellular manufacturing systems. The machine-part grouping with alternative process plans means to form machine-part groupings in which a part may be processed not only by a specific process but by many alternative processes. For this problem, this study presents an algorithm based on self organizing neural networks, so called SOM (Self Organizing feature Map). The SOM, a special type of neural networks is an intelligent tool for grouping machines and parts in group formation problem of the machine and part. SOM can learn from complex, multi-dimensional data and transform them into visually decipherable clusters. In the proposed algorithm, output layer in SOM network had been set as one-dimensional structure and the number of output node has been set sufficiently large in order to spread out the input vectors in the order of similarity. In the first stage of the proposed algorithm, SOM has been applied twice to form an initial machine-process group. In the second stage, grouping efficacy is considered to transform the initial machine-process group into a final machine-process group and a final machine-part group. The proposed algorithm was tested on well-known machine-part grouping problems with alternative process plans. The results of this computational study demonstrate the superiority of the proposed algorithm. The proposed algorithm can be easily applied to the group formation problem compared to other meta-heuristic based algorithms. In addition, it can be used to solve large-scale group formation problems.