• Journal of the Korean Wood Science and Technology
• /
• v.36 no.1
• /
• pp.21-29
• /
• 2008

The deterioration of Korean traditional wooden house located in seoul was estimated. This house was attacked by termite. To estimate damage status of buildings, non-destructive methods were applied. Some of the post needed to be replaced due to low strength, estimated by nondestructive methods. The house was installed with boiler heating facility, to use office and public education. This kind of heating system changed the environmental condition of the wooden house. The termite which attacked the house was classified as Reticulitermes speratus. Because of durability of wooden house effected by environment, control of the environmental condition is essential for maintaining the wooden house. The installation of modern facility to traditional wooden house should not change the traditional structure and do not effect to durability of wooden house.

• Journal of Internet Computing and Services
• /
• v.15 no.5
• /
• pp.95-105
• /
• 2014

In this paper, we present a novel ant-based hierarchical clustering algorithm, where ants repeatedly hop from one node to another over a weighted directed graph of k-nearest neighborhood obtained from a given dataset. We introduce a notion of node pheromone, which is the summation of amount of pheromone on incoming arcs to a node. The node pheromone can be regarded as a relative density measure in a local region. After a finite number of ants' hopping, we remove nodes with a small amount of node pheromone from the directed graph, and obtain a group of strongly connected components as clusters. We iteratively do this removing process from a low value of threshold to a high value, yielding a hierarchy of clusters. We demonstrate the performance of the proposed algorithm with synthetic and real data sets, comparing with traditional clustering methods. Experimental results show the superiority of the proposed method to the traditional methods.

• The KIPS Transactions:PartB
• /
• v.11B no.1
• /
• pp.77-82
• /
• 2004

Reinforcement learning takes reward about selecting action when agent chooses some action and did state transition in Present state. this can be the important subject in reinforcement learning as temporal-credit assignment problems. In this paper, by new meta heuristic method to solve hard combinational optimization problem, examine Ant-Q learning method that is proposed to solve Traveling Salesman Problem (TSP) to approach that is based for population that use positive feedback as well as greedy search. And, suggest Ant-TD reinforcement learning method that apply state transition through diversification strategy to this method and TD-error. We can show through experiments that the reinforcement learning method proposed in this Paper can find out an optimal solution faster than other reinforcement learning method like ACS and Ant-Q learning.

• Journal of Digital Convergence
• /
• v.11 no.11
• /
• pp.443-448
• /
• 2013

Ant Colony System(ACS) is a meta heuristic approach based on biology in order to solve combinatorial optimization problem. It is based on the tracing action of real ants which accumulate pheromone on the passed path and uses as communication medium. In order to search the optimal path, ACS requires to explore various edges. In existing ACS, the local updating rule assigns the same pheromone to visited edge. In this paper, our local updating rule gives the pheromone according to the total frequency of visits of the currently selected node in the previous iteration. I used the ACS algoritm using subpath for search. Our approach can have less local optima than existing ACS and find better solution by taking advantage of more informations during searching.

• The KIPS Transactions:PartB
• /
• v.12B no.7 s.103
• /
• pp.823-828
• /
• 2005

Ant Colony System (ACS) applied to the traveling salesman problem (TSP) has demonstrated a good performance on the small TSP. However, in case of the large TSP. ACS does not yield the optimum solution. In order to overcome the drawback of the An for the large TSP, the present study employs the idea of subpath to give more irormation to ants by computing the distance of subpath with length u. in dealing with the large TSP, the experimental results indicate that the proposed algorithm gives the solution much closer to the optimal solution than does the original ACS. In comparison with the original ACS, the present algorithm has substantially improved the performance. By utilizing the proposed algorithm, the solution performance has been enhanced up to $70\%$ for some graphs and around at $30\%$ for averaging over all graphs.

• Journal of Korean Society of Transportation
• /
• v.26 no.5
• /
• pp.185-194
• /
• 2008

The objective of this paper is to design the dynamic route guidance system(DRGS) and develop an ant algorithm based on routing mechanism for finding the multiple shortest paths within limited time in real traffic network. The proposed ant algorithm finds a collection of paths between source and destination considering turn-restrictions, U-turn, and P-turn until an acceptable solution is reached. This method can consider traffic constraints easily comparing to the conventional shortest paths algorithms.

• Journal of KIISE:Computer Systems and Theory
• /
• v.32 no.8
• /
• pp.399-410
• /
• 2005

This paper Proposes a novel adaptive genetic algorithm (GA) extrapolated by an ant colony optimization. We first prove that the algorithm converges to the unique global optimal solution with probability arbitrarily close to one and then, by experimental studies, show that the algorithm converges faster to the optimal solution than GA with elitism and the population average fitness value also converges to the optimal fitness value. We further discuss controlling the tradeoff of exploration and exploitation by a parameter associated with the proposed algorithm.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2012.11a
• /
• pp.367-368
• /
• 2012

Possibilistic c-means (PCM) 알고리즘은 fuzzy c-means (FCM) 의 노이즈 민감성을 극복하기 위해 제안 되었다. 하지만, PCM 은 사용되는 시스템 파라미터들의 초기화와 coincident 클러스터링 문제로 인하여 그 성능이 민감하다. 본 논문에서는 이러한 문제점들을 극복하기 위해 개미군집 알고리즘(Ant colony algorithm)을 이용한 퍼지 클러스터링(fuzzy clustering) 알고리즘을 제안한다. 먼저, 개미군집 알고리즘을 통해 PCM 의 클러스터 개수 및 중심 값 파라미터를 최적화 하고, 미리 분류된 화소 정보를 이용하여 PCM 의 coincident 클러스터링 문제를 해결하였다. 제안된 알고리즘의 효율성을 의료 영상 분할 문제에 적용하여 확인하였다.

• The KIPS Transactions:PartB
• /
• v.10B no.3
• /
• pp.237-242
• /
• 2003

Ant Colony System (ACS) Algorithm is new meta heuristic for hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem (TSP). In this paper, we introduce ACS of new method that adds reinforcement value for each edge that visit to Local/Global updating rule. and the performance results under various conditions are conducted, and the comparision between the original ACS and the proposed method is shown. It turns out that our proposed method can compete with tile original ACS in terms of solution quality and computation speed to these problem.

• Journal of Korean Society of Transportation
• /
• v.23 no.4 s.82
• /
• pp.81-91
• /
• 2005

The Traveling Salesman Problem(TSP) is one of the NP-complete (None-deterministic Polynomial time complete) route optimization problems. Its calculation time increases very rapidly as the number of nodes does. Therefore, the near optimum solution has been searched by heuristic algorithms rather than the real optimum has. This paper reviews the Ant System Algorithm(ANS), an heuristic algorithm of TSP and its applicability in the parcel delivery service in Korea. ASA, which is an heuristic algorithm of NP-complete has been studied by M. Dorigo in the early 1990. ASA finds the optimum route by the probabilistic method based on the cumulated pheromone on the links by ants. ASA has been known as one of the efficient heuristic algorithms in terms of its calculation time and result. Its applications have been expanded to vehicle routing problems, network management and highway alignment planning. The precise criteria for vehicle routing has not been set up in the parcel delivery service of Korea. Vehicle routing has been determined by the vehicle deriver himself or herself. In this paper the applicability of ASA to the parcel delivery service has been reviewed. When the driver s vehicle routing is assumed to follow the Nearest Neighbor Algorithm (NNA) with 20 nodes (pick-up and drop-off places) in $10Km{\times}10Km$ service area, his or her decision was compared with ASA's one. Also, ASA showed better results than NNA as the number of nodes increases from 10 to 200. If ASA is applied, the transport cost savings could be expected in the parcel delivery service in Korea.