| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2020: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Outline of Final Research Achievements |
In this research, I propose approximate solution methods that apply deep learning and deep reinforcement learning to the traveling salesman problem, which is one of the combinatorial optimization problems. The deep learning is trained to learn the optimum solution or the near-optimal solution as an image for a large number of randomly generated problem instances, and an evaluation value is calculated from the image data obtained as an output. The evaluation value obtained by learning is adopted to the heuristic solution method instead of the conventional evaluation value based on distance, in order to obtain a solution. Computational experiments have shown that the evaluation values obtained by learning are valid.
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