A consensus-based algorithm for multi-objective optimization and its mean-field description [article]

Giacomo Borghi, Michael Herty, Lorenzo Pareschi
<span title="2022-03-30">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2203.16384v1">arXiv:2203.16384v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/r7ggx2zc2reb5gywlnnug6kjiy">fatcat:r7ggx2zc2reb5gywlnnug6kjiy</a> </span>
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