• 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 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 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.308-311
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• 2008

The ant colony optimization (ACO) is a metaheuristic inspired by the behavior of real ants. Recently, ACO has been widely used to solve the difficult combinatorial optimization problems. In this paper, we propose an ACO algorithm to solve the two disjoint paths problem with dual link cost structure (TDPDCP). We propose a dual pheromone structure and a procedure for solution construction which is appropriate for the TDPDCP. Computational comparisons with the state-of-the-arts algorithms are also provided.

• Journal of Korean Society of Forest Science
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• v.99 no.2
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• pp.179-185
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• 2010

Organophosphate pesticides inhibit cholinesterase. It is likely that application of organophosphate pesticides affect behavior of arthropods. This study aimed to find changes in foraging behavior of ants due to application of fenitrothion, one of the widely used organophosphate pesticides. Foraging activity (FA) of ants was observed using bait cards in a pesticide sprayed pine stand and in an unsprayed stand before and after aerial application of fenitrothion in 2003 and 2004. Ant abundance and species richness of ants were also monitored using pitfall traps during the activity season in 2003 and 2004. There was not a significant decrease in abundance and species richness after the application of fenitrothion. However, FA of an ant, Paratrechina flavipes (Smith), which was abundant enough to be statistically compared, was depressed from 2 hours to 10 days after application of the pesticide. FA was fully recovered at day 14 in 2003, and was partially recovered at day 18 and fully at day 31 in 2004. FA of other ant species also decreased significantly during the FA depression period of P. flavipes. On the bait cards, workers of the species responded dully to baits during the FA depression period. Despite the decline in activity, alertness of P. flavipes to other species did not decrease even during the FA depression period.

• The KIPS Transactions:PartA
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• v.10A no.4
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• pp.369-374
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• 2003

Ant Algorithm is used to find the solution of Combinatorial Optimization Problems. Real ants are capable of finding the shortest path from a food source to their nest without using visual informations. This behavior of real ants has inspired ant algorithm. There are various versions of Ant Algorithm. Ant Colony System (ACS) is introduced lately. ACS is applied to the Traveling Salesman Problem (TSP) for verifying the availability of ACS and evaluating the performance of ACS. ACS find a good solution for TSP When ACS is applied to different Combinatorial Optimization Problems, ACS uses the same parameters and strategies that were used for TSP. In this paper, ACS is applied to the Multicast Routing Problem. This Problem is to find the paths from a source to all destination nodes. This definition differs from that of TSP and differs from finding paths which are the shortest paths from source node to each destination nodes. We introduce parameters and strategies of ACS for Multicasting Routing Problem.

• ETRI Journal
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• v.30 no.1
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• pp.129-140
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• 2008

In this paper, we propose a novel ant colony optimization (ACO)-based test scheduling method for testing network-on-chip (NoC)-based systems-on-chip (SoCs), on the assumption that the test platform, including specific methods and configurations such as test packet routing, generation, and absorption, is installed. The ACO metaheuristic model, inspired by the ant's foraging behavior, can autonomously find better results by exploring more solution space. The proposed method efficiently combines the rectangle packing method with ACO and improves the scheduling results by dynamically choosing the test-access-mechanism widths for cores and changing the testing orders. The power dissipation and variable test clock mode are also considered. Experimental results using ITC'02 benchmark circuits show that the proposed algorithm can efficiently reduce overall test time. Moreover, the computation time of the algorithm is less than a few seconds in most cases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3496-3514
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• 2015

One of the challenging tasks in Mobile Ad hoc Network is to discover precise optimal routing solution due to the infrastructure-less dynamic behavior of wireless mobile nodes. Ant Colony Optimization, a swarm Intelligence technique, inspired by the foraging behaviour of ants in colonies was used in the past research works to compute the optimal path. In this paper, we propose a Recurrent Ant Colony Optimization (RECACO) that executes the actual Ant Colony Optimization iteratively based on recurrent value in order to obtain an optimal path convergence. Each iteration involves three steps: Pheromone tracking, Pheromone renewal and Node selection based on the residual energy in the mobile nodes. The novelty of our approach is the inclusion of new pheromone updating strategy in both online step-by-step pheromone renewal mode and online delayed pheromone renewal mode with the use of newly proposed metric named ELD (Energy Load Delay) based on energy, Load balancing and end-to-end delay metrics to measure the performance. RECACO is implemented using network simulator NS2.34. The implementation results show that the proposed algorithm outperforms the existing algorithms like AODV, ACO, LBE-ARAMA in terms of Energy, Delay, Packet Delivery Ratio and Network life time.

• Journal of Information Technology Applications and Management
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• v.17 no.1
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• pp.127-139
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• 2010

Customer behavior pattern discovery is the fundament for conducting customer oriented services and the services management. But, the composition, need, interest and experience of customers may be continuously changing, thereof lead to the difficulty in refining a stable description of their consistent behavior pattern. This paper presented a new method for the behavior pattern discovery from a changing collection of customers. It was originally inspired from the swarm intelligence of ant colony. By the adaptive clustering, some typical behavior patterns which reflect the characteristics of related customer clusters can extracted dynamically and adaptively.

• Journal of Power Electronics
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• v.18 no.5
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• pp.1409-1423
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• 2018

Compared with classical linear controllers, a nonlinear controller can result in better control performance for the nonlinear uncertainties of continuously variable transmission (CVT) systems that are driven by a synchronous reluctance motor (SynRM). Improved control performance can be seen in the nonlinear uncertainties behavior of CVT systems by using the proposed mingled revised recurrent Hermite polynomial neural network (MRRHPNN) control with mend ant colony optimization (ACO). The MRRHPNN control with mend ACO can carry out the overlooker control system, reformed recurrent Hermite polynomial neural network (RRHPNN) control with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, to help improve convergence and to obtain better learning performance, the mend ACO is utilized for adjusting the two varied learning rates of the two parameters in the RRHPNN. Finally, comparative examples are illustrated by experimental results to confirm that the proposed control system can achieve better control performance.