• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.572-580
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• 2021

In order to reduce transmission delay and increase reliability in networks, mainly high-performance and high-power network equipment is used to guarantee network quality. In this paper, we propose an optimization algorithm to minimize the energy consumed when transmitting traffic in networks with a bundle link composed of multiple physical cables. The proposed optimization algorithm is a meta-heuristic method, which uses tabu search algorithm. In addition, it is designed to minimize transmission energy by minimizing the cables on the paths of the source and destination nodes for each traffic. In the proposed optimization algorithm, performance evaluation was performed in terms of the number of cables used in the transmission and the link utilization for all traffic on networks, and the performance evaluation result confirmed the superior performance than the previously proposed method.

• Journal of the Korea Society of Computer and Information
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• v.13 no.3
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• pp.147-152
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• 2008

Ant System(AS) is new meta heuristic for hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem. In this paper, AS is applied to the Multicast Routing Problem. Multicast Routing is modeled as the NP-complete Steiner tree problem. This is the shortest path from source node to all destination nodes. We proposed new AS to resolve this problem. The proposed method selects the neighborhood node to consider all costs of the edge and the next node in state transition rule. Also, The edges which are selected elite agents are updated to additional pheromone. Simulation results of our proposed method show fast convergence and give lower total cost than original AS and $AS_{elite}$.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3834-3857
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• 2021

The design of cluster-based routing protocols is necessary for Wireless Sensor Networks (WSN). But, due to the lack of features, the traditional methods face issues, especially on unbalanced energy consumption of routing protocol. This work focuses on enhancing the security and energy efficiency of the system by proposing Energy Efficient Based Secure Routing Protocol (EESRP) which integrates trust management, optimization algorithm and key management. Initially, the locations of the deployed nodes are calculated along with their trust values. Here, packet transfer is maintained securely by compiling a Digital Signature Algorithm (DSA) and Elliptic Curve Cryptography (ECC) approach. Finally, trust, key, location and energy parameters are incorporated in Particle Swarm Optimization (PSO) and meta-heuristic based Harmony Search (HS) method to find the secure shortest path. Our results show that the energy consumption of the proposed approach is 1.06mJ during the transmission mode, and 8.69 mJ during the receive mode which is lower than the existing approaches. The average throughput and the average PDR for the attacks are also high with 72 and 62.5 respectively. The significance of the research is its ability to improve the performance metrics of existing work by combining the advantages of different approaches. After simulating the model, the results have been validated with conventional methods with respect to the number of live nodes, energy efficiency, network lifetime, packet loss rate, scalability, and energy consumption of routing protocol.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.2
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• pp.311-316
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• 2016

Network intrusion detection system based on machine learning method such as artificial neural network is quite dependent on the selected features in terms of accuracy and efficiency. Nevertheless, choosing the optimal combination of features, which guarantees accuracy and efficienty, from generally used many features to detect network intrusion requires extensive computing resources. In this paper, we deal with a optimal feature selection problem to determine 6 denial service attacks and normal usage provided by NSL-KDD data. We propose a optimal feature selection algorithm. Proposed algorithm is based on the multi-start local search algorithm, one of representative meta-heuristic algorithm for solving optimization problem. In order to evaluate the performance of our proposed algorithm, comparison with a case of all 41 features used against NSL-KDD data is conducted. In addtion, comparisons between 3 well-known machine learning methods (multi-layer perceptron., Bayes classifier, and Support vector machine) are performed to find a machine learning method which shows the best performance combined with the proposed feature selection method.

• 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.