Time-varying multi-objective optimisation over switching graphs via fixed-time consensus algorithms

This paper considers distributed multi-objective optimisation problems with time-varying cost functions for network-connected multi-agent systems over switching graphs. The scalarisation approach is used to convert the problem into a weighted-sum objective. Fixed-time consensus algorithms are develo...

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Veröffentlicht in:International journal of systems science Jg. 51; H. 15; S. 2793 - 2806
Hauptverfasser: Li, Zhongguo, Ding, Zhengtao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Taylor & Francis 17.11.2020
Taylor & Francis Ltd
Schlagworte:
Adaptive algorithms
Algorithms
Consensus
Convergence
Cost function
distributed algorithm
gradient descent
Graphs
multi-agent systems
Multiagent systems
Multiple objective analysis
optimisation
Pareto optimization
Switching
ISSN:0020-7721, 1464-5319
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Abstract This paper considers distributed multi-objective optimisation problems with time-varying cost functions for network-connected multi-agent systems over switching graphs. The scalarisation approach is used to convert the problem into a weighted-sum objective. Fixed-time consensus algorithms are developed for each agent to estimate the global variables and drive all local copies of the decision vector to a consensus. The algorithm with fixed gains is first proposed, where some global information is required to choose the gains. Then, an adaptive algorithm is presented to eliminate the use of global information. The convergence of those algorithms to the Pareto solutions is established via Lyapunov theory for connected graphs. In the case of disconnected graphs, the convergence to the subsets of the Pareto fronts is studied. Simulation results are provided to demonstrate the effectiveness of the proposed algorithms.
AbstractList This paper considers distributed multi-objective optimisation problems with time-varying cost functions for network-connected multi-agent systems over switching graphs. The scalarisation approach is used to convert the problem into a weighted-sum objective. Fixed-time consensus algorithms are developed for each agent to estimate the global variables and drive all local copies of the decision vector to a consensus. The algorithm with fixed gains is first proposed, where some global information is required to choose the gains. Then, an adaptive algorithm is presented to eliminate the use of global information. The convergence of those algorithms to the Pareto solutions is established via Lyapunov theory for connected graphs. In the case of disconnected graphs, the convergence to the subsets of the Pareto fronts is studied. Simulation results are provided to demonstrate the effectiveness of the proposed algorithms.
Author Li, Zhongguo
Ding, Zhengtao
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  surname: Li
  fullname: Li, Zhongguo
  organization: Department of Electrical and Electronic Engineering, The University of Manchester
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  givenname: Zhengtao
  surname: Ding
  fullname: Ding, Zhengtao
  email: zhengtao.ding@manchester.ac.uk
  organization: Department of Electrical and Electronic Engineering, The University of Manchester
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SubjectTerms Adaptive algorithms
Algorithms
Consensus
Convergence
Cost function
distributed algorithm
gradient descent
Graphs
multi-agent systems
Multiagent systems
Multiple objective analysis
optimisation
Pareto optimization
Switching
Title Time-varying multi-objective optimisation over switching graphs via fixed-time consensus algorithms
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