TY - GEN
T1 - A Consensus-Based Algorithm for Multi-Objective Optimization and Its Mean-Field Description
AU - Borghi, Giacomo
AU - Herty, Michael
AU - Pareschi, Lorenzo
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85136166850&partnerID=8YFLogxK
U2 - 10.1109/CDC51059.2022.9993095
DO - 10.1109/CDC51059.2022.9993095
M3 - Conference contribution
AN - SCOPUS:85136166850
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 4131
EP - 4136
BT - 2022 IEEE 61st Conference on Decision and Control, CDC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 61st IEEE Conference on Decision and Control, CDC 2022
Y2 - 6 December 2022 through 9 December 2022
ER -