Problem-solving strategy for complex constraint network using general swarm intelligence

2017 Fiscal Year Final Research Report

• 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
• Keywords: 群知能

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.

Free Research Field

進化計算、群知能、人工知能、知識処理