Problem-solving strategy for complex constraint network using general swarm intelligence
Project/Area Number |
15K00296
|
Research Category |
Grant-in-Aid for Scientific Research (C)
|
Allocation Type | Multi-year Fund |
Section | 一般 |
Research Field |
Intelligent informatics
|
Research Institution | University of Tsukuba |
Principal Investigator |
Kanoh Hitoshi 筑波大学, システム情報系, 教授 (40251045)
|
Project Period (FY) |
2015-04-01 – 2018-03-31
|
Project Status |
Completed (Fiscal Year 2017)
|
Budget Amount *help |
¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2017: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2016: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2015: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
|
Keywords | 群知能 / 制約充足問題 / 探索 / 制約 / 粒子群最適化法 |
Outline of Final Research Achievements |
This research developed the discretization method for applying to four typical swarm-based optimization algorithms solving complex constraint satisfaction problems. The target problems are classified using an amount of characteristic in a network, in order to expand the generalization capability of the algorithms. Both the search performance and the calculation speed of the algorithms were improved by the proposed parallelization method. Systematic experiments using large scale benchmarks and facility layout problems in the real world showed that the proposed algorithms are more effective than conventional evolutionary algorithms.
|
Report
(4 results)
Research Products
(24 results)