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Evolving enhanced topologies for the synchronization of dynamical complex networks.

Thomas E Gorochowski¹, Mario di Bernardo, Claire S Grierson

¹Bristol Centre for Complexity Sciences, Department of Engineering Mathematics, University of Bristol, Bristol BS8 1TR, United Kingdom.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|September 28, 2010

Summary

This study evolved network topology for better synchronization using a computational tool, NETEVO. Dynamically guided evolution revealed distinct network features compared to topology-based methods.

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Area of Science:

Complex Systems
Network Science
Computational Dynamics

Background:

Network synchronization is crucial for many natural and man-made systems.
Optimizing network topology is key to enhancing system performance.

Purpose of the Study:

To investigate evolving network topology for improved synchronization.
To compare dynamical versus topological approaches for network evolution.

Main Methods:

Utilized NETEVO, a computational tool employing simulated annealing.
Employed a dynamical approach, using system output to guide network rewiring.
Analyzed resultant topologies using network measures, B matrices, and motif distributions.

Main Results:

Networks evolved using dynamical performance measures showed distinct features.
Both dynamical and topological evolution led to some common network characteristics.
Significant differences emerged between networks evolved with different performance criteria.

Conclusions:

Dynamical system output provides a unique driver for network topology evolution.
The NETEVO tool effectively reshapes network structures for enhanced synchronization.
Understanding topology-dynamics interplay is essential for designing efficient complex systems.