Genetic algorithm for very large traveling salesman problems and its applications to practical applications
| Project/Area Number |
19700134
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Intelligent informatics
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| Research Institution | Tokyo Institute of Technology (2009)
Japan Advanced Institute of Science and Technology (2007-2008) |
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Principal Investigator |
NAGATA Yuichi Japan Advanced Institute of Science and Technology, 大学院・総合理工学研究科, 助教 (70334795)
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| Project Period (FY) |
2007 – 2009
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| Project Status |
Completed (Fiscal Year 2009)
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| Budget Amount *help |
¥2,860,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥360,000)
Fiscal Year 2009: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2008: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2007: ¥1,300,000 (Direct Cost: ¥1,300,000)
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| Keywords | 遺伝的アルゴリズム / 巡回セールスマン問題 / 車両配送問題 / メメティックアルゴリズム / memetic algorithm / 組合せ最適化 / メタヒューリスティクス |
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| Research Abstract |
We have developed an genetic algorithm (GA) for the traveling salesman problem (TSP). The GA uses edge assembly crossover (EAX), which is known to be efficient and effective for solving TSPs. We first propose a modified EAX algorithm that can be executed more efficiently than the original, which is 2-7 times faster. We then propose a selection model that can efficiently maintain population diversity at negligible computational cost. The edge entropy measure is used as an indicator of population diversity. We further improved the performance of the GA with EAX, especially for large instances of more than 10,000 cities. Our method is highly competitive with existing approaches. Moreover, we have developed very efficient MAs for several vehicle routing problems by extending the GA developed for the TSP.
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Report
(4 results)
Research Products
(34 results)
