• Journal of Ubiquitous Convergence Technology
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• 제2권1호
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• pp.40-50
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• 2008

The problem of assigning customers to satellite channels is a difficult combinatorial optimization problem and is NP-complete. For this combinatorial optimization problem, standard optimization methods take a large computation time and so genetic algorithms (GA) and ant colony optimization (ACO) can be used to obtain the best and/or optimal assignment of customers to satellite channels. In this paper, we present a comparative study of GA and ACO to this problem. Various issues related to genetic algorithms approach to this problem, such as solution representation, selection methods, genetic operators and repair of invalid solutions are presented. We also discuss an ACO for this problem. In ACO methodology, three strategies, ACO with only ranking, ACO with only max-min ant system (MMAS), and ACO with both ranking and MMAS, are considered. A comparison of these two approaches (i,e., GA and ACO) with the standard optimization method is presented to show the advantages of these approaches in terms of computation time.

• 대한전기학회논문지:전력기술부문A
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• 제50권4호
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• pp.188-196
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• 2001

This paper presents and efficient algorithm for the loss minimization by automatic sectionalizing switch operation in distribution systems. Ant colony algorithm is multi-agent system in which the behaviour of each single agent, called artificial ant, is inspired by the behaviour of real ants. Ant colony algorithm is suitable for combinatiorial optimization problem as network reconfiguration because it use the long term memory, called pheromone, and heuristic information with the property of the problem. The proposed methodology with some adoptions have been applied to improve the computation time and convergence property. Numerical examples demonstrate the validity and effectiveness of the proposed methodology using a KEPCO's distribution system.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.85-90
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• 2023

The Ant Colony System (ACS) is a variant of Ant colony optimization algorithm which is well-known in Traveling Salesman Problem. This paper proposed a hybrid method based on genetic algorithm (GA) and ant colony system (ACS), called GACS, to solve traffic routing problem. In the GACS, we use genetic algorithm to optimize the ACS parameters that aims to attain the shortest trips and time through new functions to help the ants to update global and local pheromones. Our experiments are performed by the GACS framework which is developed from VANETsim with the ability of real map loading from open street map project, and updating traffic light in real-time. The obtained results show that our framework acquired higher performance than A-Star and classical ACS algorithms in terms of length of the best global tour and the time for trip.

• 전자공학회논문지T
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• 제35T권2호
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• pp.72-80
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• 1998

현용 이동전화의 셀룰라 시스템 기지국은 대부분 3섹터로 운용되고 있다. 그러나 특정 기지국의 지형지물에 의한 전파환경 때문에 수신 전계강도 취약지역 또는 전파 음영지역이 발생한다. 이 문제를 해결하기 위해 특정 섹터에 두개의 주빔이 필요하게 되므로 경제적 과대 투자 비용과 운용비를 감수하면서라도 4섹터 기지국으로 설계를 해야한다. 따라서 논자는 특정 기지국을 4섹터로 설계하는 대신 Dual Tx Ant.를 설치 운용함으로써 경제적 부담을 최소화하고 통화품질을 향상시킬수 있도록 하였다. 시험 대상 기지국으로 서울의 양평기지국과 개포기지국 그리고 부산 덕천기지국을 선정하였고, Dual Tx Ant.설치전과 설치후의 데이터를 비교하여 그 실용성을 확인 하였다.

• 전기학회논문지
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• 제56권7호
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• pp.1309-1314
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• 2007

The assignment of cells to reporting or non-reporting cells is an NP-hard problem having an exponential complexity in the Reporting Cell Location Management (RCLM) system. Frequent location update may result in degradation of quality of service due to interference. Miss on the location of a mobile terminal will necessitate a search operation on the network when a call comes in. The number of reporting cells and which cell must be reporting cell should be determined to balance the registration (location update) and search (paging) operations to minimize the cost of RCLM system. T1is paper compares Max-Min ant system (MMAS), rank-based ant system (RAS) and hybrid method of MMAS and RAS that generally used to solve combinatorial optimization problems. Experimental results demonstrate that hybrid method of MMAS and RAS is an effective and competitive approach in fairly satisfactory results with respect to solution quality and execution time for the optimal design of location management system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1031-1033
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• 2005

This paper presents a system of handwritten numerals recognition, which is based on Ant-miner algorithm (data mining based on Ant colony optimization). At the beginning, three distinct fractures (also called attributes) of each numeral are extracted. The attributes are Loop zones, End points, and Feature codes. After these data are extracted, the attributes are in the form of attribute = value (eg. End point10 = true). The extraction is started by dividing the numeral into 12 zones. The numbers 1-12 are referenced for each zone. The possible values of Loop zone attribute in each zone are "true" and "false". The meaning of "true" is that the zone contains the loop of the numeral. The Endpoint attribute being "true" means that this zone contains the end point of the numeral. There are 24 attributes now. The Feature code attribute tells us how many lines of a numeral are passed by the referenced line. There are 7 referenced lines used in this experiment. The total attributes are 31. All attributes are used for construction of the classification rules by the Ant-miner algorithm in order to classify 10 numerals. The Ant-miner algorithm is adapted with a little change in this experiment for a better recognition rate. The results showed the system can recognize all of the training set (a thousand items of data from 50 people). When the unseen data is tested from 10 people, the recognition rate is 98 %.

• 정보처리학회논문지B
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• 제10B권3호
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• pp.237-242
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• 2003

Ant Colony System(ACS) 알고리즘은 조합 최적화 문제를 해결하기 위한 메타 휴리스틱 탐색 방법이다. 이것은 greedy search뿐만 아니라 exploitation of positive feedback을 사용한 모집단에 근거한 접근법으로 Traveling Salesman Problem(TSP)를 풀기 위해 제안되었다. 본 논문에서는 전통적 전역갱신과 지역갱신 방법에 개미들이 방문한 각 간선에 대한 방문 횟수를 강화값으로 추가한 새로운 방법의 ACS를 제안한다. 그리고 여러 조건 하에서 TCS 문제를 풀어보고 그 성능에 대해 기존의 ACS 방법과 제안된 ACS 방법을 비교 평가해, 최적해에 더 빨리 수렴함을 실험을 통해 알 수 있었다.

• Journal of Microbiology and Biotechnology
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• 제27권3호
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• pp.535-541
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• 2017

Two Gram-staining-negative, red-pinkish, coccus-shaped, non-motile, and aerobic bacterial strains, designated $Ant21^T$ and Ant22, were isolated from the Antarctic coastal sea water. Strains $Ant21^T$ and Ant22 showed UVC and gamma radiation resistance. Phylogenetic analyses based on 16S rRNA gene sequences determined that these strains belong to the genus Deinococcus. Through the analyses of the 16S rRNA gene sequences, strains $Ant21^T$ and Ant22 were found to have 97.7% and 97.8% similarity to Deinococcus marmoris DSM $12784^T$ and 97.0% and 97.2% similarity to Deinococcus saxicola AA-$1444^T$, respectively. The sequence similarity with the type strains of other Deinococcus species was less than 96.9% for both strains. Strains $Ant21^T$ and Ant22 shared relatively high 16S rRNA gene sequence similarity (99.3%) and had a closely related DNA reassociation value of $84{\pm}0.5%$. Meanwhile, they showed a low level of DNA-DNA hybridization (<30%) with other closely related species of the genus Deinococcus. The two strains also showed typical chemotaxonomic features for the genus Deinococcus, in terms of the major polar lipid (phosphoglycolipid) and the major fatty acids ($C_{16:0}$, $C_{16:1}$ ${\omega}6c/{\omega}7c$, $iso-C_{17:0}$, and $iso-C_{15:0}$). They grew at temperatures between $4^{\circ}C$ and $30^{\circ}C$ and at pH values of 6.0-8.0. Based on the physiological characteristics, the 16S rRNA gene sequence analysis results, and the low DNA-DNA reassociation level with Deionococcus marmoris, strains $Ant21^T$ ($=KEMB\;9004-167^T$ $=JCM\;31436^T$) and Ant22 (KEMB 9004-168 =JCM 31437) represent novel species belonging to the genus Deinococcus, for which the name Deinococcus rubrus is proposed.

• 한국콘텐츠학회논문지
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• 제5권5호
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• pp.1-9
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• 2005

본 논문에서는 Temporal Difference 학습을 적용한 Ant-Q 기반 개미 모델을 이용한 다중 집단 상호작용 개미 강화학습 모델을 제안한다. 이 모델은 몇 개의 독립적 개미시스템 집단으로 이루어져 있으며, 상호작용은 집단간 엘리트 전략(강화, 다양화 전략)에 따라 임무를 수행한다. 강화 전략은 다른 에이전트 집단의 휴리스틱 정보를 이용해 좋은 경로 선택을 가능하게 한다. 이것은 집단간 긍정적 상호작용을 통해 에이전트들의 방문 빈도가 높은 간선을 선택하게 한다. 다양화 전략은 에이전트들이 다른 에이전트 집단의 탐색 정보에 의해 부정적 상호작용을 수행함으로써 방문 빈도수가 높은 간선의 선택을 회피하게 만든다. 이러한 전략을 통해 제안한 강화학습은 기존의 개미집단시스템, Ant-Q학습보다 최적해에 더 빠르게 수렴할 수 있음을 실험을 통해 알 수 있었다.

• 한국콘텐츠학회논문지
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• 제11권3호
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• pp.100-107
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• 2011

휴리스틱 탐색에서 강화(Intensification)와 다양화(Diversification)의 조화는 중요한 연구 부분이다. 본 논문에서는 개미 집단 최적화(Ant Colony Optimization, ACO) 접근법의 하나인 개미 집단 시스템(Ant Colony System, ACS)에서 강화와 다양화의 조화를 통한 성능 향상시키는 방법을 제안한다. 제안 방법은 다양화 전략으로 전역 최적 경로가 향상되지 않는 경우 반복 탐색 구간을 고려해 상태전이 규칙의 파라미터를 변경해 탐색하고, 이러한 다양화 전략을 통해 발견된 전역 최적 경로에서 이전 전역 최적 경로와 현재 전역 최적 경로의 중복 간선에 대해 페로몬을 강화시켜 탐색하는 혼합된 탐색 방법을 제안한다. 그리고, 실험을 통해 제안된 방법이 기존 ACS-3-opt 알고리즘, ACS-Subpath 알고리즘, ACS-Iter 알고리즘, ACS-Global-Ovelap 알고리즘에 비해 최적 경로 탐색 및 평균 최적 경로 탐색의 성능이 우수함을 보여 준다.