• Journal of KIISE:Computer Systems and Theory
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• v.32 no.8
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• pp.399-410
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• 2005

This paper Proposes a novel adaptive genetic algorithm (GA) extrapolated by an ant colony optimization. We first prove that the algorithm converges to the unique global optimal solution with probability arbitrarily close to one and then, by experimental studies, show that the algorithm converges faster to the optimal solution than GA with elitism and the population average fitness value also converges to the optimal fitness value. We further discuss controlling the tradeoff of exploration and exploitation by a parameter associated with the proposed algorithm.

• Korean Management Science Review
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• v.25 no.2
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• pp.73-80
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• 2008

The location area planning is to assign cells to the location areas of a wireless communication network in an optimum manner. The two important cost components are cost of location update and cost of paging that are of conflicting in nature; i.e., minimizing the registration cost might increase the search cost. Hence, it is important to find a compromise between the location update and paging operations such that the cost of mobile terminal location tracking cost is a minimum. The complete mobile network is divided into location areas. Each location area consists of a group of cells. In fact this is shown to be an NP-complete problem in an earlier study. In this paper, we use an ant colony optimization method to obtain the best/optimal group of cells for a given a network.

• Korean Management Science Review
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• v.26 no.3
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• pp.55-65
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• 2009

The objective of this paper is to propose an ant colony optimization (ACO) for clustering design in wireless sensor network problem. This proposed ACO approach is designed to deal with the dynamics of the sensor nodes which can be adaptable to topological changes to any network graph in a time. Long communication distances between sensors and a sink in a sensor network can greatly consume the energy of sensors and reduce the lifetime of a network. We can greatly minimize the total communication distance while minimizing the number of cluster heads using proposed ACO. Simulation results show that our proposed method is very efficient to find the best solutions comparing to the optimal solution using CPLEX in 100, 200, and 400 node sensor networks.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.6
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• pp.125-132
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• 2017

Generally, in machining operations, the required machining performance can be obtained by properly combining several machining parameters properly. In this research, we construct a simulation model, which that predicts the relationship between the input variables and output variables in the turning operation. Input variables necessary for the turning operation include cutting speed, feed, and depth of cut. Surface roughness and electrical current consumption are used as the output variables. To construct the simulation model, an Artificial Neural Network (ANN) is employed. With theIn ANN, training is necessary to find appropriate weights, and the Ant Colony Optimization (ACO) technique is used as a training tool. EspeciallyIn particular, for the continuous domain, ACOR is adopted and athe related algorithm is developed. Finally, the effects of the algorithm on the results are identified and analyzsed.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.36-43
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• 2019

Turning, a main machining process, is a widespread process in metal cutting industries. Many researchers have investigated the effects of process parameters on the machining process. In the turning process, input variables including cutting speed, feed, and depth of cut are generally used. Surface roughness and electric current consumption are used as output variables in this study. We construct a simulation model for the turning process using a neural network, which predicts the output values based on input values. In the neural network, obtaining the appropriate set of weights, which is called training, is crucial. In general, back propagation (BP) is widely used for training. In this study, techniques such as ant colony optimization (ACO) and particle swarm optimization (PSO) as well as BP were used to obtain the weights in the neural network. Particularly, two combined techniques of ACO_BP and PSO_BP were utilized for training the neural network. Finally, the performances of the two techniques are compared with each other.