• The KIPS Transactions:PartB
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• v.15B no.3
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• pp.245-252
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• 2008

We present a classification algorithm based on ant colony system(ACS) for classifying digital images. The ACS has been recently emerged as a useful tool for the pattern recognition, image extraction, and edge detection. The classification algorithm of digital images is very important in the application areas of digital image coding, image analysis, and image recognition because it significantly influences the quality of images. The conventional procedures usually classify digital images with the fixed value for the associated parameters and it requires postprocessing. However, the proposed algorithm utilizing randomness of ants yields the stable and enhanced images even for processing the rapidly changing images. It is also expected that, due to this stability and flexibility of the present procedure, the digital images are stably classified for processing images with various noises and error signals arising from processing of the drastically fast moving images could be automatically compensated and minimized.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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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.

• Journal of applied mathematics & informatics
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• v.16 no.1_2
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• pp.337-354
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• 2004

SA, TS, GA and ACS are four of the main algorithms for solving challenging problems of intelligent systems. In this paper we consider Examination Timetabling Problem that is a common problem for all universities and institutions of higher education. There are many methods to solve this problem, In this paper we use Simulated Annealing, Tabu Search, Genetic Algorithm and Ant Colony System in their basic frameworks for solving this problem and compare results of them with each other.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.6
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• pp.68-74
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• 2010

This paper suggest a new ACS algorithm to solve SMP which was solved by Gale-Shapley algorithm. The stable marriage problem is an extensively-studied combinatorial problem with many practical applications. The classical Gale-Shapley algorithm produces a marriage that greatly favors the men at the expense of the women, or vice versa. In this paper we apply ACS algorithm to SMP to find 4 kinds of solutions such as stable matching with man-optimal, woman-optimal, egalitarian stable matching, sex-fair stable matching. So this ACS is a novel method to solve Stable Marriage Problem. Our simulation results show the effectiveness of the proposed ACS.

• The Journal of the Korea Contents Association
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• v.11 no.6
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• pp.51-58
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• 2011

One of the objectives of research on routing methods in wireless sensor networks is maximizing the energy life of sensor nodes that have limited energy. In this study, we tried to even energy use in a wireless sensor network by giving a weight to the transition probability of ACS(Ant Colony System), which is commonly used to find the optimal path, based on the amount of energy in a sensor and the distance of the sensor from the sink. The proposed method showed improvement by 46.80% on the average in energy utility in comparison with representative routing method GPSR (Greedy Perimeter Stateless Routing), and its residual energy after operation for a specific length of time was 6.7% more on the average than that in ACS.

• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.9-15
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• 2009

Ant Colony System is new meta heuristic for hard combinatorial optimization problem. The original ant colony system accomplishes a pheromone updating about only the global optimal path using global updating rule. But, If the global optimal path is not searched until the end condition is satisfied, only pheromone evaporation happens to no matter how a lot of iteration accomplishment. In this paper, the length of the global optimal path does not improved within the limited iterations, we evaluates this state that fall into the local optimum and selects the next node using changed parameters in the state transition rule. This method has effectiveness of the search for a path through diversifications is enhanced by decreasing the value of parameter of the state transition rules for the select of next node, and escape from the local optima is possible. Finally, the performance of Best and Average_Best of proposed algorithm outperforms original ACS.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.2
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• pp.87-94
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• 2004

The feature subset selection is used as a preprocessing step of a teaming algorithm. If collected data are irrelevant or redundant information, we can improve the performance of learning by removing these data before creating of the learning model. The feature subset selection can also reduce the search space and the storage requirement. This paper proposed a new feature subset selection algorithm that is using the heuristic function based on entropy to evaluate the performance of the abstracted feature subset and feature selection. The ACS algorithm was used as a search method. We could decrease a size of learning model and unnecessary calculating time by reducing the dimension of the feature that was used for learning.