| Budget Amount *help |
¥5,070,000 (Direct Cost: ¥3,900,000、Indirect Cost: ¥1,170,000)
Fiscal Year 2013: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2012: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2011: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
|
|---|
| Research Abstract |
A new multi-objective local search approach considering local Pareto optimality has been proposed. This approach also has an interpolation mechanism for capturing the whole of Pareto subsets. Through the numerical examples, the effectiveness of the proposed approach could be indicated.
Furthermore, a new approach based on Evolutionary Multi-criterion Optimization (EMO) for sparse CT problem were developed. This approach incorporates Gerchberg -Saxton algorithm (GS algorithm) that is the fact standard method in the field of phase retrieval problem as optimization tool and implements an original genetic operators utilizing the characteristics of strength distribution. The superiority of our approach could be confirmed by comparison to the existing approaches in sparse CT problem.
|
