Optimizing Dynamical Network Structure for Pinning Control.

Abstract:

:Controlling dynamics of a network from any initial state to a final desired state has many applications in different disciplines from engineering to biology and social sciences. In this work, we optimize the network structure for pinning control. The problem is formulated as four optimization tasks: i) optimizing the locations of driver nodes, ii) optimizing the feedback gains, iii) optimizing simultaneously the locations of driver nodes and feedback gains, and iv) optimizing the connection weights. A newly developed population-based optimization technique (cat swarm optimization) is used as the optimization method. In order to verify the methods, we use both real-world networks, and model scale-free and small-world networks. Extensive simulation results show that the optimal placement of driver nodes significantly outperforms heuristic methods including placing drivers based on various centrality measures (degree, betweenness, closeness and clustering coefficient). The pinning controllability is further improved by optimizing the feedback gains. We also show that one can significantly improve the controllability by optimizing the connection weights.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Orouskhani Y,Jalili M,Yu X

doi

10.1038/srep24252

subject

Has Abstract

pub_date

2016-04-12 00:00:00

pages

24252

issn

2045-2322

pii

srep24252

journal_volume

6

pub_type

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