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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Vydáno v:International journal of systems science Ročník 51; číslo 15; s. 2793 - 2806
Hlavní autoři: Li, Zhongguo, Ding, Zhengtao
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Taylor & Francis 17.11.2020
Taylor & Francis Ltd
Témata:
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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Shrnutí: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.
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ISSN:0020-7721
1464-5319
DOI:10.1080/00207721.2020.1801885