• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2001년도 가을 학술발표논문집 Vol.28 No.2 (2)
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• pp.79-81
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• 2001

그래프 착색 문제(Graph Coloring Problem)는 인접한 노드 (V$_{i}$, V$_{j}$ )가 같은 색을 갖지 않도록 그래프 G의 노드 V에 색을 배정하는 문제로, NP-hard 문제로 잘 알려져 있다. 또한 최근까지 그래프 착색 문제의 최적 해를 구하기 위하여 다양한 접근방식들과 해법들이 제안되고 있다. 본 논문에서는 기존의 그래프 착색 문제의 해법으로 잘 알려진 Greedy algorithms, Simulated Annealing. Tabu search 등이 아닌 실세계에서 개미들이 자신의 분비물을 이용하여 경로를 찾는 Ant System을 개선하여 새롭게 제안한 Ant Colony System(ACS) 알고리즘으로 해를 구하는 ANTCOL을 소개하고, ANTCOL에서 DSATUR, Recursive Largest First(RLF) 등의 방식을 사용한 기존 생성 함수들과 RLF를 개선하여 제안한 eXtend RLF방식을 사용한 생성 함수를 비교, 평가하고자 한다.

• 제어로봇시스템학회논문지
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• 제17권8호
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• pp.760-767
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• 2011

The flexibility and evolvability are critical characteristics of modern manufacturing to adapt to changes from products and disturbances in the shop floor. The technologies inspired from biology and nature enable to equip the manufacturing systems with these characteristics. This paper proposes an ant colony inspired autonomous manufacturing system in which the resources on the shop floor are considered as the autonomous entities. Each entity overcomes the disturbance by itself or negotiates with the others. The swarm of cognitive agents with the ant-like pheromone based negotiation mechanism is proposed for controlling the shop floor. The functionality of the developed system is proven on the test bed.

• ETRI Journal
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• 제35권3호
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• pp.414-424
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• 2013

We consider a new load balancing routing for delay-guaranteed services in the network in which the traffic is dynamic and network topologies frequently change. For such an ever-changing network, we propose a new online load balancing routing called AntLBR, which exploits the ant colony optimization method. Generally, to achieve load balancing, researchers have tried to calculate the traffic split ratio by solving a complicated linear programming (LP) problem under the static network environment. In contrast, the proposed AntLBR does not make any attempt to solve this complicated LP problem. So as to achieve load balancing, AntLBR simply forwards incoming flows by referring to the amount of pheromone trails. Simulation results indicate that the AntLBR algorithm achieves a more load-balanced network under the changing network environment than techniques used in previous research while guaranteeing the requirements of delay-guaranteed services.

• Industrial Engineering and Management Systems
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• 제4권2호
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• pp.184-191
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• 2005

In this article we study the feasibility of the Ant Colony Optimisation (ACO) algorithm for finding optimal Kanban allocations in Kanban systems represented by Stochastic Petri Net (SPN) models. Like other optimisation algorithms inspired by nature, such as Simulated Annealing/Genetic Algorithms, the ACO algorithm contains a large number of adjustable parameters. Thus we study the influence of the parameters on performance of ACO on the Kanban allocation problem, and identify the most important parameters.

• 한국경영과학회지
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• 제33권3호
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• pp.17-28
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• 2008

The Steiner arborescence problem is known to be NP-hard. The objective of this problem is to find a minimal Steiner tree which starts from a designated node and spans all given terminal nodes. This paper proposes a method based on a two-step procedure to solve this problem efficiently. In the first step, graph reduction rules eliminate useless nodes and arcs which do not contribute to make an optimal solution. In the second step. ant colony algorithm with use of Prim's algorithm is used to solve the Steiner arborescence problem in the reduced graph. The proposed method based on a two-step procedure is tested in the five test problems. The results show that this method finds the optimal solutions to the tested problems within 50 seconds. The algorithm can be applied to undirected Steiner tree problems with minor changes. 18 problems taken from Beasley are used to compare the performances of the proposed algorithm and Singh et al.'s algorithm. The results show that the proposed algorithm generates better solutions than the algorithm compared.

• Journal of Electrical Engineering and Technology
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• 제6권2호
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• pp.174-181
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• 2011

This paper proposes a novel binary ant colony optimization (NBACO) method. The proposed NBACO is based on the concept and principles of ant colony optimization (ACO), and developed to solve the binary and combinatorial optimization problems. The concept of conventional ACO is similar to Heuristic Dynamic Programming. Thereby ACO has the merit that it can consider all possible solution sets, but also has the demerit that it may need a big memory space and a long execution time to solve a large problem. To reduce this demerit, the NBACO adopts the state probability matrix and the pheromone intensity matrix. And the NBACO presents new updating rule for local and global search. The proposed NBACO is applied to test power systems of up to 100-unit along with 24-hour load demands.

• 한국정밀공학회지
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• 제29권4호
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• pp.373-379
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• 2012

Disruptions in manufacturing systems caused by system changes and disturbances such as the tool wear, machine breakdown, malfunction of transporter, and so on, reduce the productivity and the increase of downtime and manufacturing cost. In order to cope with these challenges, a new method to build an intelligent manufacturing system with biological principles, namely an ant colony inspired manufacturing system, is presented. In the developed system, the manufacturing system is considered as a swarm of cognitive agents where work-pieces, machines and transporters are controlled by the corresponding cognitive agent. The system reacts to disturbances autonomously based on the algorithm of each autonomous entity or the cooperation with them. To develop the ant colony inspired manufacturing system, the disturbances happened in the machining shop of a transmission case were analyzed to classify them and to find out the corresponding management methods. The system architecture with the autonomous characteristics was generated with the cognitive agent and the ant colony technology. A test bed was implemented to prove the functionality of the developed system.

• 한국콘텐츠학회논문지
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• 제10권5호
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• pp.1-9
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• 2010

이 논문은 특징 선택에 사용되는 개미 군락 최적화의 수렴 특성을 개선하기 위해 선택적 평가라는 새로운 기법을 제시한다. 이 방법은 불필요하거나 가능성이 덜한 후보 해를 배제함으로써 계산량을 줄인다. 이 방법은, 그런 해를 찾아내는데 사용할 수 있는 페로몬 정보 때문에 구현이 가능하다. 문제 크기에 따른 알고리즘의 적용가능성을 판단할 목적으로, 특징 선택에 사용되는 세 가지 알고리즘인 탐욕 알고리즘, 유전 알고리즘, 그리고 개미 군락 최적화의 계산 시간을 분석한다. 엄밀한 분석을 위해 원자 연산이라는 개념을 사용한다. 실험 결과는 선택적 평가를 채택한 개미 군락 최적화가 계산 시간과 인식 성능 모두에서 우수함을 보여준다.

• 정보처리학회논문지B
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• 제13B권3호
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• pp.315-322
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• 2006

개미 군락 시스템(Ant Colony System, ACS)은 조합 최적화 문제 중의 하나인 방문 판매원 문제에(Traveling Salesman Problem, TSP) 간단하게 응용할 수 있고 좋은 결과를 보여주었으며 최근에는 영상처리 분야의 패턴 인식, 영상 추출, 에지 검색 등에 응용되고 있다. 에지 검색은 검색된 에지를 이용하여 문서 분류, 문자 인식, 얼굴 인식 등과 같은 분야에서 다양하게 응용될 수 있다. 기존의 연산자 위주의 에지 검색 기법들은 에지를 명확하게 검색한다고 해도 이 검색 결과를 이용하여 다음 단계의 영상처리를 위해서는 그 목적에 맞도록 새로운 후처리 작업을 거쳐야 한다는 단점이 있다. 본 연구에서는 개미 군락 시스템의 특성을 이용하여 에지의 명확한 검색뿐 아니라, 좀 더 안정적이고(robustness) 유연성을(flexibility) 갖는 에지 검색 기법을 제안하며 실제 디지털 영상에 적용하였을 때 만족할 만한 결과를 얻을 수 있었다.

• Journal of Power Electronics
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• 제11권6호
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• pp.856-863
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• 2011

In this paper a novel method of the Ant Colony Optimization algorithm for rotor position estimation in Switched Reluctance Motors is presented. The data provided by the initial assumptions is one of the important aspects used to solve the problems relative to an Ant Colony algorithm. Considering the nature of a real ant colony, it was found that the ants have no primary data for deducing which is the shortest path in their initial iteration. They also do not have the ability to see the food sources at a distance. According to this point of view, a novel method is presented in which the rotor pole position relative to the corresponding stator pole in a switched reluctance motor is estimated with high accuracy using the active and inactive phase parameters. This new method gives acceptable results such as a desirable convergence together with an optimized and stable response. To the best knowledge of the authors, such an analysis has not been carried out previously.