Consensus-Based Distributed Optimization for Multiagent Systems Over Multiplex Networks

C. D. Rodríguez-Camargo

doi:10.1109/TCNS.2024.3510602

Consensus-Based Distributed Optimization for Multiagent Systems Over Multiplex Networks

2024-01-01

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PRE

AI

C

C. D. Rodríguez-Camargo

A

Andrés F. Urquijo-Rodríguez

E

Eduardo Mojica‐Nava *

DOI：10.1109/TCNS.2024.3510602

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Multilayer networks provide a more comprehensive framework for exploring real-world and engineering systems than traditional single-layer networks consisting of multiple interacting networks. However, despite significant research on distributed optimization for single-layer networks, similar progress is lacking for multilayer systems. This article proposes two algorithms for distributed optimization problems in multiplex networks using the supra-Laplacian matrix and its diffusion dynamics. The algorithms include a distributed saddle-point algorithm and its variation as a distributed gradient descent algorithm. By relating consensus and diffusion dynamics, we obtain the multiplex supra-Laplacian matrix. We extend the distributed gradient descent algorithm for multiplex networks using this matrix and analyze the convergence of both algorithms with several theoretical results. Numerical examples validate our proposed algorithms, and we explore the impact of interlayer diffusion on consensus time. We also present a coordinated dispatch for interdependent infrastructure networks (energy-gas) to demonstrate the application of the proposed framework to real engineering problems.

Keyword:

Multiplexing

Optimization

Heuristic algorithms

Nonhomogeneous media

Network systems

Multi-agent systems

Control systems

Physics

Laplace equations

Convex functions

Diffusion

distributed optimization

multiplex networks

saddle-point flow

Consensus-Based Distributed Optimization for Multiagent Systems Over Multiplex Networks

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