| Budget Amount *help |
¥5,070,000 (Direct Cost: ¥3,900,000、Indirect Cost: ¥1,170,000)
Fiscal Year 2013: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2012: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2011: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
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| Research Abstract |
This work proposes an effective evolutionary algorithm for many-objective optimization. It clarifies the importance of population size, the relevance of non-disruptive recombination, and the scalability in large-scale many-objective optimization. The algorithm reduces its parameters and increases its reliability for different problems and population sizes by using two adaptive methods during selection. The effectiveness of epsilon dominance mappings to search sets of optimal solutions with desired distributions is shown and new methods based on conflict information among objectives are proposed for space partitioning. The proposed algorithm is verified in real-world optimization problems. Namely, the proposed algorithm is applied to optimize the trajectory of JAXA's DESTINY mission spacecraft.
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