CBO algorithm with average drift and applications to portfolio optimization
Optimization and Control
2026-02-24 v1 Dynamical Systems
Abstract
We propose a consensus based optimization algorithm with average drift (in short Ad-CBO) and provide a theoretical framework for it. In the theoretical analysis, we show that particle solutions to Ad-CBO converge to a global minimizer. In numerical simulations, we examine Ad-CBO's performance in optimizing static and dynamic objective functions. As a real-time application, we test the efficiency of Ad-CBO to find the optimal portfolio given stochastically evolving multi-asset prices in a financial market. The proposed Ad-CBO exhibits higher searching speed, lower tracking errors and regret bound than the CBO without stochastic diffusion
Cite
@article{arxiv.2602.19204,
title = {CBO algorithm with average drift and applications to portfolio optimization},
author = {Hyeong-Ohk Bae and Seung-Yeal Ha and Chanho Min and Jane Yoo and Jaeyoung Yoon},
journal= {arXiv preprint arXiv:2602.19204},
year = {2026}
}