In this paper we provide a rigorous convergence analysis for the renowned particle swarm
optimization method by using tools from stochastic calculus and the analysis of partial …

We investigate the implementation of a new stochastic Kuramoto-Vicsek-type model for
global optimization of nonconvex functions on the sphere. This model belongs to the class of …

In this chapter we give an overview of the consensus-based global optimization algorithm
and its recent variants. We recall the formulation and analytical results of the original model …

Federated learning is an important framework in modern machine learning that seeks to
integrate the training of learning models from multiple users, each user having their own …

In this work we are interested in the construction of numerical methods for high-dimensional
constrained nonlinear optimization problems by particle-based gradient-free techniques. A …

In this paper, we study consensus-based optimisation (CBO), a versatile, flexible and
customisable optimisation method suitable for performing nonconvex and nonsmooth global …

In this paper we study consensus-based optimization (CBO), which is a multiagent
metaheuristic derivative-free optimization method that can globally minimize nonconvex …

In this work we are interested in stochastic particle methods for multi-objective optimization.
The problem is formulated via scalarization using parametrized, single-objective sub …

In this paper we study anisotropic consensus-based optimization (CBO), a population-based
metaheuristic derivative-free optimization method capable of globally minimizing nonconvex …

In this paper, we propose consensus-based optimization for saddle point problems (CBO-
SP), a novel multi-particle metaheuristic derivative-free optimization method capable of …