Abstract
In this paper, we provide an analytical framework for investigating the efficiency of a consensus-based model for tackling global optimization problems. This work justifies the optimization algorithm in the mean-field sense showing the convergence to the global minimizer for a large class of functions. Theoretical results on consensus estimates are then illustrated by numerical simulations where variants of the method including nonlinear diffusion are introduced.
| Original language | English |
|---|---|
| Article number | 00276 |
| Pages (from-to) | 1037-1066 |
| Number of pages | 30 |
| Journal | Mathematical Models and Methods in Applied Sciences |
| Volume | 28 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 15 Jun 2018 |
Keywords
- agent-based models
- consensus formation
- Global optimization
- mean-field limit
- opinion dynamics
- stochastic dynamics