A parallel adaptive quantum genetic algorithm for the controllability of arbitrary networks.

ABSTRACT: In this paper, we propose a novel algorithm-parallel adaptive quantum genetic algorithm-which can rapidly determine the minimum control nodes of arbitrary networks with both control nodes and state nodes. The corresponding network can be fully controlled with the obtained control scheme. We transformed the network controllability issue into a combinational optimization problem based on the Popov-Belevitch-Hautus rank condition. A set of canonical networks and a list of real-world networks were experimented. Comparison results demonstrated that the algorithm was more ideal to optimize the controllability of networks, especially those larger-size networks. We demonstrated subsequently that there were links between the optimal control nodes and some network statistical characteristics. The pr

SUBMITTER: Li Y 

PROVIDER: S-EPMC5858774 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature