.png){kind=icon}
.png){kind=icon}
Return
Preset-time distributed algorithm for time-varying constrained optimization on multiplex networks
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ins.2025.122734.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This study investigates the preset-time distributed time-varying (TV) constrained optimization problem on multiplex networks, where both the cost functions and constraints exhibit the TV characteristic. By leveraging the time-regulator function, the supra-Laplacian matrix and a multi-phase control framework, we develop a distributed optimization algorithm that achieves preset-time convergence on multiplex networks. In the initial phase of this algorithm, the TV constraints are satisfied within the preset time frame. Then, in the subsequent phase, while continuously maintaining compliance with these constraints, the gradient consensus for all TV objective functions is achieved within the preset time frame. The effectiveness and superiority of the proposed algorithm are substantiated via several numerical simulations.
Keywords:
Consensus
Multiplex network
Preset-time convergence
Time-varying constrained optimization
Journal
IF:
6.8
Papers:
540
Citations:
6.2W