• Journal of the Korea Society of Computer and Information
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• v.17 no.10
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• pp.155-165
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• 2012

This paper suggests traveling salesman problem algorithm that have been unsolved problem with NP-Hard. The proposed algorithm is a heuristic with edge-swap method. The classical method finds the initial solution starts with first node and visits to mostly adjacent nodes then decides the traveling path. This paper selects minimum weight edge for each nodes, then perform Min-Min method that start from minimum weight edge among the selected edges and Min-Max method that starts from maximum weight edges among it. Then we decide tie initial solution to minimum path length between Min-Min and Min-Max method. To get the final optimal solution, we apply previous two-opt to initial solution. Also, we suggest extended 3-opt and 4-opt additionally. For the 7 actual experimental data, this algorithm can be get the optimal solutions of state-of-the-art with fast and correct.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.249-257
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• 2015

This paper introduces a heuristic algorithm to NP-hard travelling salesman problem. The proposed algorithm, in its bid to determine initial path, applies SW-DNN, DW-DNN, and DC-DNN, which are modified forms of the prevalent Double-sided Nearest Neighbor Search and searches the minimum value. As a part of its optimization process on the initial solution, it employs 2, 2.5, 3-opt of a local search k-opt on candidate delete edges and 4-opt on undeleted ones among them. When tested on TSP-1 of 26 European cities and TSP-2 of 49 U.S. cities, the proposed algorithm has successfully obtained optimal results in both, disproving the prevalent disbelief in the attainability of the optimal solution and making itself available as a general algorithm for the travelling salesman problem.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.7
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• pp.666-673
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• 2008

To construct the accurate radio satellite navigation system, the efficient communication each satellite with the ground station is very important. Throughout the communication, the orbit of each satellite can be corrected, and those information will be used to analyze the satellite satus by the operator. Since there are limited resources of ground station, the schedule of antenna's azimuth and elevation angle should be optimized. On the other hand, the satellite in the medium earth orbit does not pass the same point of the earth surface due to the rotation of the earth. Therefore, the antenna pass schedule must be updated at the proper moment. In this study, Q learning approach which is a form of model-free reinforcement learning and genetic algorithm are considered to find the optimal antenna schedule. To verify the optimality of the solution, numerical simulations are conducted.