A consensus-based algorithm for multi-objective optimization and its mean-field description
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.
Authors 3
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RWTH Aachen University · University of Ferrara
Affiliation as printed
Department of Mathematics and Computer Science , University of Ferrara , 44121 Italy
Institute of Geometry and Practical Mathematics , RWTH Aachen University , 52062 Aachen , Germany
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Affiliation as printed
Institute of Geometry and Practical Mathematics , RWTH Aachen University , 52062 Aachen , Germany
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Affiliation as printed
Institute of Geometry and Practical Mathematics , RWTH Aachen University , 52062 Aachen , Germany
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