• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.131-137
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• 2007

Recently, Ant Colony Optimization (ACO) is emerged as a simple yet powerful optimization algorithm for routing and load-balancing of both wired and wireless networks. However, there are few researches trying to adopt ACO to enhance routing performance in WSN owing to difficulties in applying ACO to WSN because of stagnation effect. In this paper, we propose an energy-efficient path selection algorithm based on ACO for WSN. The algorithm is not by simply applying ACO to routing algorithm but by introducing a mechanism to alleviate the influence of stagnation. By the simulation result, the proposed algorithm shows better performance in data propagation delay and energy efficiency over Directed Diffusion which is one of the outstanding schemes in multi-hop flat routing protocols for WSN. Moreover, we checked that the proposed algorithm is able to mitigate stagnation effect than simple ACO adoption to WSN.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.13-21
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• 2017

Airline schedules are highly dependent on various factors of uncertainties such as unfavorable weather conditions, mechanical problems, natural disaster, airport congestion, and strikes. If the schedules are not properly managed to cope with such disturbances, the operational cost and performance are severely affected by the delays, cancelations, and so forth. This is described as a disruption. When the disruption occurs, the airline requires the feasible recovery plan returning to the normal operations in a timely manner so as to minimize the cost and impact of disruptions. In this research, an Ant Colony Optimization (ACO) algorithm with re-timing strategy is developed to solve the recovery problem for both aircraft and passenger. The problem consists of creating new aircraft routes and passenger itineraries to produce a feasible schedule during a recovery period. The suggested algorithm is based on an existing ACO algorithm that aims to reflect all the downstream effects by considering the passenger recovery cost as a part of the objective function value. This algorithm is complemented by re-timing strategy to effectively manage the disrupted passengers by allowing delays even on some of undisrupted flights. The delays no more than 15 minutes are accepted, which does not influence on the on-time performance of the airlines. The suggested method is tested on the real data sets from 2009 ROADEF Challenge, and the computational results are compared with the existing ones on the same data sets. The method generates the solution for most of problem set in 10 minutes, and the result generated by re-timing strategy is discussed for its impact.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.282-284
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• 2001

This paper presents an efficient algorithm for the loss minimization in distribution systems. Ant colony algorithm is suitable for combinatorial 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 32-bus system.

• Journal of KIISE:Software and Applications
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• v.30 no.9
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• pp.862-866
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• 2003

Traveling Salesman Problem(TSP) is a combinational optimization problem, Genetic Algorithm(GA) and Lin-Kernighan(LK) Heuristic［1］that is Local Search Heuristic are one of the most commonly used methods to resolve TSP. In this paper, we introduce ACS(Ant Colony System) Algorithm as another approach to solve TSP and propose a new pheromone updating method. ACS uses pheromone information between cities in the Process where many ants make a tour, and is a method to find a optimal solution through recursive tour creation process. At the stage of Global Updating of ACS method, it updates pheromone of edges belonging to global best tour of created all edge. But we perform once more pheromone update about created all edges before global updating rule of original ACS is applied. At this process, we use the frequency of occurrence of each edges to update pheromone. We could offer stochastic value by pheromone about each edges, giving all edges' occurrence frequency as weight about Pheromone. This finds an optimal solution faster than existing ACS algorithm and prevent a local optima using more edges in next time search.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.2
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• pp.704-710
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• 2017

Our research team is developing a 6-DOF manipulator that is adequate for the narrow workspace of press forming processes. This paper addresses the task sequence optimization methods for the manipulator to minimize the task-finishing time. First, a kinematic model of the manipulator is presented, and the anticipated times for moving among the task locations are computed. Then, a mathematical model of the task sequence optimization problem is presented, followed by a comparison of three meta-heuristic methods to solve the optimization problem: an ant colony system, simulated annealing, and a genetic algorithm. The simulation shows that the genetic algorithm is robust to the parameter settings and has the best performance in both minimizing the task-finishing time and the computing time compared to the other methods. Finally, the algorithms were implemented and validated through a simulation using Mathworks' Matlab and Coppelia Robotics' V-REP (virtual robot experimentation platform).

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.4
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• pp.117-123
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• 2008

In this paper, we propose a novel ant colony optimization(ACO)-based application core ma ins method for implementing network-on-chip(NoC)-based systems-on-chip(SoCs). The proposed method efficiently put application cores to a mesh-type NoC satisfying a given design objective, the network latency. Experimental results using a functional circuit including 12 cores show that the proposed algorithm can produce near optimal mapping results within a second.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.2326-2345
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• 2016

For the compression of color images, a common bitmap usually is generated to replace the three individual bitmaps that originate from block truncation coding (BTC) of the R, G and B channels. However, common bitmaps generated by some traditional schemes are not the best possible because they do not consider the minimized distortion of the entire color image. In this paper, we propose a near-optimized common bitmap scheme for BTC using Binary Ant Colony Optimization (BACO), producing a BACO-BTC scheme. First, the color image is compressed by the BTC algorithm to get three individual bitmaps, and three pairs of quantization values for the R, G, and B channels. Second, a near-optimized common bitmap is generated with minimized distortion of the entire color image based on the idea of BACO. Finally, the color image is reconstructed easily by the corresponding quantization values according to the common bitmap. The experimental results confirmed that reconstructed image of the proposed scheme has better visual quality and less computational complexity than the referenced schemes.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.342-352
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• 2021

Multicast Routings is a big challenge due to limitations such as node power and bandwidth Mobile Ad-hoc Network (MANET). The path to be chosen from the source to the destination node requires protocols. Multicast protocols support group-oriented operations in a bandwidth-efficient way. While several protocols for multi-cast MANETs have been evolved, security remains a challenging problem. Consequently, MANET is required for high quality of service measures (QoS) such infrastructure and application to be identified. The goal of a MANETs QoS-aware protocol is to discover more optimal pathways between the network source/destination nodes and hence the QoS demands. It works by employing the optimization method to pick the route path with the emphasis on several QoS metrics. In this paper safe routing is guaranteed using the Secured Multicast Routing offered in MANET by utilizing the Ant Colony Optimization (ACO) technique to integrate the QOS-conscious route setup into the route selection. This implies that only the data transmission may select the way to meet the QoS limitations from source to destination. Furthermore, the track reliability is considered when selecting the best path between the source and destination nodes. For the optimization of the best path and its performance, the optimized algorithm called the micro artificial bee colony approach is chosen about the probabilistic ant routing technique.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.352-358
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• 2022

In this paper, we study the relationship between infection rates of covid 19 and the precautionary measures and strict protocols taken by Saudi Arabia to combat the spread of the coronavirus disease and minimize the number of infected people. Based on the infection rates and the timetable of precautionary measures, the best framework of precautionary measures was identified by applying the traveling salesman problem (TSP) that relies on ant colony optimization (ACO) algorithms. The proposed algorithm was applied to daily infected cases data in Saudi Arabia during three periods of precautionary measures: partial curfew, whole curfew, and gatherings penalties. The results showed the partial curfew and the whole curfew for some cities have the minimum total cases over other precautionary measures. The gatherings penalties had no real effect in reducing infected cases as the other two precautionary measures. Therefore, in future similar circumstances, we recommend first applying the partial curfew and the whole curfew for some cities, and not considering the gatherings penalties as an effective precautionary measure. We also recommend re-study the application of the grouping penalty, to identify the reasons behind the lack of its effectiveness in reducing the number of infected cases.

• 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.