• Journal of Ubiquitous Convergence Technology
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• v.2 no.1
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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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.4
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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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• v.23 no.8
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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.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.35T no.2
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• pp.72-80
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• 1998

Most cellsite are operated by 3-sector system in these days. Due to the weak propagating signal caused by peculiar topography, there exist weak or shadow regions. To solve this problem, we need double beam in a sector, thus we have to design 4-sector cellisite even if investment cost and maintenance cost gets high. We propose here using the dual TX-ANT instead of designing 4-sector cellsite to reduse investment cost and to improve call quality. We choose YangPeong and Gepo cellisite in Seoul and DuckCheon cellsite in Pusan to run the experiments. And we verified the practicality by comparing data before and before and after the installation of dual TX-ANT.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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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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• 2005.06a
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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 %.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.237-242
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• 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 Microbiology and Biotechnology
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• v.27 no.3
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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.

• The Journal of the Korea Contents Association
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• v.5 no.5
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• pp.1-9
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• 2005

In this paper, we suggest multi colony interaction ant reinforcement learning model. This method is a hybrid of multi colony interaction by elite strategy and reinforcement teaming applying Temporal Difference(TD) learning to Ant-Q loaming. Proposed model is consisted of some independent AS colonies, and interaction achieves search according to elite strategy(Intensification, Diversification strategy) between the colonies. Intensification strategy enables to select of good path to use heuristic information of other agent colony. This makes to select the high frequency of the visit of a edge by agents through positive interaction of between the colonies. Diversification strategy makes to escape selection of the high frequency of the visit of a edge by agents achieve negative interaction by search information of other agent colony. Through this strategies, we could know that proposed reinforcement loaming method converges faster to optimal solution than original ACS and Ant-Q.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.100-107
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• 2011

One of the important fields for heuristic algorithm is how to balance between Intensification and Diversification. In this paper, we deal with the performance improvement techniques through balance the intensification and diversification in Ant Colony System(ACS) which is one of Ant Colony Optimization(ACO). In this paper, we propose the hybrid searching method between intensification strategy and diversification strategy. First, 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. And then we consider the overlapping edge of the global best path of the previous and the current, and, to enhance the pheromone for the overlapping edges increases the probability that the optimal path is configured. Finally, the performance of Best and Average-Best of proposed algorithm outperforms ACS-3-opt, ACS-Subpath, ACS-Iter and ACS-Global-Ovelap algorithms.