• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.3
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• pp.17-28
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• 2008

The Steiner arborescence problem is known to be NP-hard. The objective of this problem is to find a minimal Steiner tree which starts from a designated node and spans all given terminal nodes. This paper proposes a method based on a two-step procedure to solve this problem efficiently. In the first step, graph reduction rules eliminate useless nodes and arcs which do not contribute to make an optimal solution. In the second step. ant colony algorithm with use of Prim's algorithm is used to solve the Steiner arborescence problem in the reduced graph. The proposed method based on a two-step procedure is tested in the five test problems. The results show that this method finds the optimal solutions to the tested problems within 50 seconds. The algorithm can be applied to undirected Steiner tree problems with minor changes. 18 problems taken from Beasley are used to compare the performances of the proposed algorithm and Singh et al.'s algorithm. The results show that the proposed algorithm generates better solutions than the algorithm compared.

• The KIPS Transactions:PartB
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• v.16B no.3
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• pp.195-202
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• 2009

In this paper, we propose the regions segmentation method of the white matter and the gray matter for brain MR image by using the ant colony optimization algorithm. Ant Colony Optimization (ACO) is a new meta heuristics algorithm to solve hard combinatorial optimization problem. This algorithm finds the expected pixel for image as the real ant finds the food from nest to food source. Then ants deposit pheromone on the pixels, and the pheromone will affect the motion of next ants. At each iteration step, ants will change their positions in the image according to the transition rule. Finally, we can obtain the segmentation results through analyzing the pheromone distribution in the image. We compared the proposed method with other threshold methods, viz. the Otsu' method, the genetic algorithm, the fuzzy method, and the original ant colony optimization algorithm. From comparison results, the proposed method is more exact than other threshold methods for the segmentation of specific region structures in MR brain image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2504-2526
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• 2016

Wireless sensors are always deployed in brutal environments, but as we know, the nodes are powered only by non-replaceable batteries with limited energy. Sending, receiving and transporting information require the supply of energy. The essential problem of wireless sensor network (WSN) is to save energy consumption and prolong network lifetime. This paper presents a new communication protocol for WSN called Dynamical Threshold Control Algorithm with three-parameter Particle Swarm Optimization and Ant Colony Optimization based on residual energy (DPA). We first use the state of WSN to partition the region adaptively. Moreover, a three-parameter of particle swarm optimization (PSO) algorithm is proposed and a new fitness function is obtained. The optimal path among the CHs and Base Station (BS) is obtained by the ant colony optimization (ACO) algorithm based on residual energy. Dynamical threshold control algorithm (DTCA) is introduced when we re-select the CHs. Compared to the results obtained by using APSO, ANT and I-LEACH protocols, our DPA protocol tremendously prolongs the lifecycle of network. We observe 48.3%, 43.0%, and 24.9% more percentages of rounds respectively performed by DPA over APSO, ANT and I-LEACH.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1169-1182
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• 2020

The ant colony optimization (ACO) algorithm is a classical metaheuristic optimization algorithm. However, the conventional ACO was liable to trap in the local minimum and has an inherent slow rate of convergence. In this work, we propose a novel combinatorial ACO algorithm (CG-ACO) to alleviate these limitations. The genetic algorithm and the cloud model were embedded into the ACO to find better initial solutions and the optimal parameters. In the experiment section, we compared CG-ACO with the state-of-the-art methods and discussed the parameter stability of CG-ACO. The experiment results showed that the CG-ACO achieved better performance than ACO_R, simple genetic algorithm (SGA), CQPSO and CAFSA and was more likely to reach the global optimal solution.

• 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 the Korean Operations Research and Management Science Society
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• v.34 no.1
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• pp.153-165
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• 2009

This paper apollos an ant colony system (ACS) for the vehicle routing problem with time window (VRPTW). The VRPTW is a generalization of the VRP where the service of a customer can begin within the time windows defined by the earliest and latest times when the customer will permit the start of service. The ACS has been applied effectively in geographical environment such as TSP or VRP by meta-heuristic that imitate an ant's biologic special duality in route construction, 3 saving based ACS (SB-ACS) is introduced and its solution is improved by local search. Through iterative precesses, the SB-ACS is shown to drive the best solution. The algorithm has been tested on 56 Solomon benchmarking problems and compared to the best-known solutions on literature. Experimental results shows that SB-ACS algorithm could obtain food solution in total travel distance minimization.

• IE interfaces
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• v.21 no.3
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• pp.254-261
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• 2008

Connected-(r,s)-out of-(m,n) : F system is an important topic in redundancy design of the complex system reliability and it's maintenance policy. Previous studies applied Monte Carlo simulation and genetic, simulated annealing algorithms to tackle the difficulty of maintenance policy problem. These algorithms suggested most suitable maintenance cycle to optimize maintenance pattern of connected-(r,s)-out of-(m,n) : F system. However, genetic algorithm is required long execution time relatively and simulated annealing has improved computational time but rather poor solutions. In this paper, we propose the ant colony optimization approach for connected-(r,s)-out of-(m,n) : F system that determines maintenance cycle and minimum unit cost. Computational results prove that ant colony optimization algorithm is superior to genetic algorithm, simulated annealing and tabu search in both execution time and quality of solution.

• Korean Management Science Review
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• v.26 no.1
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• pp.65-76
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• 2009

The undirected Steiner tree problem in graphs is known to be NP-hard. The objective of this problem is to find a shortest tree containing a subset of nodes, called terminal nodes. This paper proposes a method based on a two-step procedure to solve this problem efficiently. In the first step. graph reduction rules eliminate useless nodes and edges which do not contribute to make an optimal solution. In the second step, a max-min ant colony optimization combined with Prim's algorithm is developed to solve the reduced problem. The proposed algorithm is tested in the sets of standard test problems. The results show that the algorithm efficiently presents very correct solutions to the benchmark problems.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.4
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• pp.38-45
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• 2003

This paper presents a meta-heuristic solution technique, Ant System (AS)algerian to solve edge detection problem. We define the quality of edge in terms of dissimilarity, continuity, thickness and length. We cast edge detection as a problem in cost minimization. This is achieved by the formulation of a cost function that inversely evaluates the quality of edge configuration. Twelve windows for enhancing dissimilarity regions based on the valid edge structures are used. The AS algorithm finds the optimal set of edge pixels based on the cost function. The experimental results show that the properly reduced set of edge pixels could be found regardless how complicated the image is.

• 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.