206 citations · 1.7k across the 107 of their papers we have counts for
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Superlinear drift in consensus-based optimization with condensation phenomena
Jonathan Franceschi, Lorenzo Pareschi, Mattia Zanella
Consensus-based optimization (CBO) is a class of metaheuristic algorithms designed for global optimization problems. In the many-particle limit, classical CBO dynamics can be rigor…
Kinetic models for optimization: a unified mathematical framework for metaheuristics
Giacomo Borghi, Michael Herty, Lorenzo Pareschi
Metaheuristic algorithms, widely used for solving complex non-convex and non-differentiable optimization problems, often lack a solid mathematical foundation. In this review, we ex…
Kinetic description and convergence analysis of genetic algorithms for global optimization
Giacomo Borghi, Lorenzo Pareschi
Genetic Algorithms (GA) are a class of metaheuristic global optimization methods inspired by the process of natural selection among individuals in a population. Despite their wides…
Consensus based optimization with memory effects: random selection and applications
Giacomo Borghi, Sara Grassi, Lorenzo Pareschi
In this work we extend the class of Consensus-Based Optimization (CBO) metaheuristic methods by considering memory effects and a random selection strategy. The proposed algorithm i…
An adaptive consensus based method for multi-objective optimization with uniform Pareto front approximation
Giacomo Borghi, Michael Herty, Lorenzo Pareschi
In this work we are interested in stochastic particle methods for multi-objective optimization. The problem is formulated using parametrized, single-objective sub-problems which ar…
A consensus-based algorithm for multi-objective optimization and its mean-field description
Giacomo Borghi, Michael Herty, Lorenzo Pareschi
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…