Abstract
In this paper, a class of penalty-function-type multi-agent approaches via communication networks is developed for distributed nonconvex optimal resource allocation. A penalty-function-type method is utilized to handle networked resource allocation constraints, and a multi-agent method is employed for handling global information in a distributed manner. Then, a penalty-function-type multi-agent system is constructed for a nonconvex optimal resource allocation model, and its stability with a local minimizer is proven. Further, a nonconvex optimal resource allocation model subject to “on/off” constraints is introduced. Based on an augmented Lagrangian function, another penalty-function-type multi-agent system is developed, and it is proven to be stable with a local minimizer. A numerical example with simulation in a heating, ventilation, and air conditioning system is presented to demonstrate the theoretical results. © 2024 IEEE.
| Original language | English |
|---|---|
| Pages (from-to) | 4169-4180 |
| Journal | IEEE Transactions on Network Science and Engineering |
| Volume | 11 |
| Issue number | 5 |
| Online published | 21 May 2024 |
| DOIs | |
| Publication status | Published - Sept 2024 |
Funding
This work was supported in part by the National Science and Technology Major Project of China under Grant 2022ZD0120001, and in part by the National Natural Science Foundation of China under Grant 62233004, Grant 62073076, Grant 62173308, and Grant 623B2018
Research Keywords
- Brain modeling
- Distributed optimal resource allocation
- Lagrangian functions
- Linear programming
- Mathematical models
- multi-agent system
- networked optimization
- nonconvex optimization
- Optimization
- penalty function method
- Resource management
- Vectors
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