No. 2022.05
A consensus-based algorithm for multi-objective optimization and its mean-field description
G. Borghi, M. Herty, and L. Pareschi
Subject: Multi-objective optimization problems, mean-field model
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.
Reference
2022 IEEE 61st Conference on Decision and Control (CDC), Cancun, Mexico, 2022, pp. 4131-4136