A consensus-based algorithm for multi-objective optimization and its mean-field description
Optimization and Control
2022-03-31 v1
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.
Cite
@article{arxiv.2203.16384,
title = {A consensus-based algorithm for multi-objective optimization and its mean-field description},
author = {Giacomo Borghi and Michael Herty and Lorenzo Pareschi},
journal= {arXiv preprint arXiv:2203.16384},
year = {2022}
}