Abstract
This article investigates the distributed aggregative optimization (DAO) problem for high-order heterogeneous integrator systems over undirected communication networks. Each agent's local objective function incorporates both private decision variables and global aggregative information. Then, a novel DAO algorithm is designed. It is rigorously established that each agent converges to the global optimum at an exponentially fast rate. Finally, the efficiency of the algorithm is demonstrated by numerical examples. © 2025 IEEE.
| Original language | English |
|---|---|
| Title of host publication | Proceedings 2025 40th Youth Academic Annual Conference of Chinese Association of Automation (YAC) |
| Publisher | IEEE |
| Pages | 31-36 |
| ISBN (Electronic) | 979-8-3315-0330-7, 979-8-3315-3948-1 |
| ISBN (Print) | 979-8-3315-0331-4 |
| DOIs | |
| Publication status | Published - 2025 |
| Event | 40th Youth Academic Annual Conference of Chinese Association of Automation (YAC 2025) - Zhengzhou, China Duration: 17 May 2025 → 19 May 2025 |
Publication series
| Name | Youth Academic Annual Conference of Chinese Association of Automation, YAC |
|---|---|
| ISSN (Print) | 2837-8598 |
| ISSN (Electronic) | 2837-8601 |
Conference
| Conference | 40th Youth Academic Annual Conference of Chinese Association of Automation (YAC 2025) |
|---|---|
| Place | China |
| City | Zhengzhou |
| Period | 17/05/25 → 19/05/25 |
Funding
This work was supported by the National Natural Science Foundation of China through Grant No. 62422315.
Research Keywords
- Aggregative optimization
- Distributed algorithm
- Heterogeneous integrator dynamics
- Multi-agent networks
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