• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.221-226
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• 2004

Ant Colony System(ACS) Algorithm is new meta-heuristic for hard combinational optimization problem. It is a population-based approach that uses exploitation of positive feedback as well as greedy search. Recently, various methods and solutions are proposed to solve optimal solution of graph coloring problem that assign to color for adjacency node($v_i, v_j$) that they has not same color. In this paper introducing ANTCOL Algorithm that is method to solve solution by Ant Colony System algorithm that is not method that it is known well as solution of existent graph coloring problem. After introducing ACS algorithm and Assignment Type Problem, show the wav how to apply ACS to solve ATP And compare graph coloring result and execution time when use existent generating functions(ANT_Random, ANT_LF, ANT_SL, ANT_DSATUR, ANT_RLF method) with ANT_XRLF method that use XRLF that apply Randomize to RLF to solve ANTCOL. Also compare graph coloring result and execution time when use method to add re-search to ANT_XRLF(ANT_XRLF_R) with existent generating functions.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.5 s.335
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• pp.29-38
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• 2005

In this paper, a new modified and optimized AntNet algorithm which can be implemented efficiently onto network processor is proposed. The AntNet that mimics the activities of the social insect is an adaptive agent-based routing algorithm. This method requires a complex arithmetic calculating system. However, since network processors have simple arithmetic units for a packet processing, it is very difficult to implement the original AntNet algorithm on network processors. Therefore, the proposed AntNet algorithm is a solution of this problem by decreasing arithmetic executing cycles for calculating a reinforcement value without loss of the adaptive performance. The results of the simulations show that the proposed algorithm is more suitable and efficient than the original AntNet algorithm for commercial network processors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.1
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• pp.64-76
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• 2017

Routing in mobile ad hoc networks (MANETs) is highly challenging because of the dynamic nature of network topology. AntHocNet is a bio-inspired routing protocol for MANETs that mimics the foraging behavior of ants. However, unlike many other MANET routing protocols, the paths constructed in AntHocNet are unidirectional, which requires a separate path setup if a route in the reverse direction is also required. Because most communication sessions are bidirectional, this unidirectional path setup approach is often inefficient. Moreover, AntHocNet suffers from looping problems because of its property of multiple paths and stochastic data routing. In this paper, we propose a modified path setup procedure that constructs bidirectional paths. We also propose solutions to some of the looping problems in AntHocNet. Simulation results show that performance is significantly enhanced in terms of overhead, end-to-end delay, and delivery ratio when loops are prevented. Performance is further improved, in terms of overhead, when bidirectional paths setup is employed.

• Management Science and Financial Engineering
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• v.15 no.1
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• pp.81-96
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• 2009

Data mining, specially clustering is one of exciting research areas for ant based algorithms. Ant clustering algorithm, however, has many difficulties for resolving practical situations in clustering. We propose a new grid-based ant colony algorithm for clustering of data. The previous ant based clustering algorithms usually tried to find the clusters during picking up or dropping down process of the items of ants using some stigmergy information. In our ant clustering algorithm we try to make the ants reflect neighborhood information within the storage nests. We use two ant classes, search ants and labor ants. In the initial step of the proposed algorithm, the search ants try to guide the characteristics of the storage nests. Then the labor ants try to classify the items using the guide in-formation that has set by the search ants and the stigmergy information that has set by other labor ants. In this procedure the clustering decision of ants is quickly guided and keeping out of from the stagnated process. We experimented and compared our algorithm with other known algorithms for the known and statistically-made data. From these experiments we prove that the suggested ant mining algorithm found the clusters quickly and effectively comparing with a known ant clustering algorithm.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.3
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• pp.71-82
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• 2004

Recently, network model inspired by social insect behavior attracts the public attention. The AntNet is an adaptive and distributed routing algorithm using mobile agents, called ants, that mimic the activities of social insect. In this paper. we present a new hardware architecture to realize an AntNet-based routing in practical system on a chip application. The modified AntNet algorithm for hardware implementation is compared with the original algorithm on the various traffic patterns and topologies. Implementation results show that the proposed architecture is suitable and efficient to realize adaptive routing based on the AntNet.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.165-172
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• 2011

In this paper, we propose Ant-Q learning Algorithm[1], which uses the habits of biological ants, to find a new way to solve Stable Marriage Problem(SMP)[3] presented by Gale-Shapley[2]. The issue of SMP is to find optimum matching for a stable marriage based on their preference lists (PL). The problem of Gale-Shapley algorithm is to get a stable matching for only male (or female). We propose other way to satisfy various requirements for SMP. ACS(Ant colony system) is an swarm intelligence method to find optimal solution by using phermone of ants. We try to improve ACS technique by adding Q learning[9] concept. This Ant-Q method can solve SMP problem for various requirements. The experiment results shows the proposed method is good for the problem.

• Journal of Ecology and Environment
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• v.37 no.3
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• pp.123-129
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• 2014

Ant communities are well suited for monitoring changes in ecosystems. Although numerous studies have examined the responses of ant communities to environmental disturbance, relatively few long-term studies on ant communities have been undertaken in urban environments. We examined species richness in nine urban parks in Hiroshima, Japan, and compared the survey results with data collected at the same sites by using the same methods in 1999. In both surveys, total of 25 species was recorded: 23 species in 1999 and 20 species in 2012. Non-metric multidimensional scaling analysis revealed that the ant communities consisted of two distinct groups, which could in turn be characterized by three patterns of ant community changes in between the two groups. The first of these community change patterns was characterized by a shift within group 1, but the number of species remained constant (approx. 10 species). The second pattern was characterized by a shift within group 2, but the number of species remained low (approx. 4 species). The third pattern was characterized by a shift from group 1 to group 2 as the abundance of Linepithema humile (Mayr) increased over time. Unlike the first and second patterns, the number of ant species in communities of the third type decreased significantly. These findings suggest that L. humile has a marked effect on the species diversity of indigenous ant communities in urban environments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9B
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• pp.1281-1286
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• 2010

Ant-based routing methods belong to a class of ant colony optimization algorithms which apply the behavior of ants in nature to routing mechanism. Since the topology of mobile ad-hoc network(MANET) changes dynamically, it is needed to establish paths based on the local information. Subsequently, it is known that routing in MANET is one of applications of ant colony optimization. In this paper, we propose a routing method, namely EPMAR, which enhances SIR in terms of route selection method and the process upon link failure. The performance of the proposed method is compared with those of AntHocNet and SIR. Based on he analysis, it is proved that the proposed method provided higher packet delivery ratio and less critical link failure than AntHocNet and SIR.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.77-82
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• 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 the Korean Operations Research and Management Science Society
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• v.35 no.2
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• pp.53-69
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• 2010

This paper proposes an ant-based routing algorithm, Ant System-Routing in wireless Senor Networks(AS-RSN), for wireless sensor networks. Using a transition rule in Ant System, sensors can spread data traffic over the whole network to achieve energy balance, and consequently, maximize the lifetime of sensor networks. The transition rule advances one of the original Ant System by re-defining link cost which is a metric devised to consider energy-sufficiency as well as energy-efficiency. This metric gives rise to the design of the AS-RSN algorithm devised to balance the data traffic of sensor networks in a decentralized manner and consequently prolong the lifetime of the networks. Therefore, AS-RSN is scalable in the number of sensors and also robust to the variations in the dynamics of event generation. We demonstrate the effectiveness of the proposed algorithm by comparing three existing routing algorithms: Direct Communication Approach, Minimum Transmission Energy, and Self-Organized Routing and find that energy balance should be considered to extend lifetime of sensor network and increase robustness of sensor network for diverse event generation patterns.