Abstract
We present a multi-agent algorithm for multi-objective optimization problems, which extends the class of consensus-based optimization methods and relies on a scalarization strategy. The optimization is achieved by a set of interacting agents exploring the search space and attempting to solve all scalar sub-problems simultaneously. We show that those dynamics are described by a mean-field model, which is suitable for a theoretical analysis of the algorithm convergence. Numerical results show the validity of the proposed method.
| Original language | English |
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| Title of host publication | 61st IEEE Conference on Decision and Control 2022 |
| Publisher | IEEE |
| Pages | 4131-4136 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781665467612 |
| DOIs | |
| Publication status | Published - 10 Jan 2023 |
| Event | 61st IEEE Conference on Decision and Control 2022 - Cancun, Mexico Duration: 6 Dec 2022 → 9 Dec 2022 |
Conference
| Conference | 61st IEEE Conference on Decision and Control 2022 |
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| Abbreviated title | CDC 2022 |
| Country/Territory | Mexico |
| City | Cancun |
| Period | 6/12/22 → 9/12/22 |
ASJC Scopus subject areas
- Control and Systems Engineering
- Modelling and Simulation
- Control and Optimization