'''Consensus-based optimization (CBO)''' <ref>{{Cite journal |last1=Pinnau |first1=René |last2=Totzeck |first2=Claudia |last3=Tse |first3=Oliver |last4=Martin |first4=Stephan |date=January 2017 |title=A consensus-based model for global optimization and its mean-field limit |url=https://www.worldscientific.com/doi/abs/10.1142/S0218202517400061 |journal=Mathematical Models and Methods in Applied Sciences |language=en |volume=27 |issue=1 |pages=183–204 |doi=10.1142/S0218202517400061 |arxiv=1604.05648 |s2cid=119296432 |issn=0218-2025}}</ref> is a multi-agent [[derivative-free optimization]] method, designed to obtain solutions for global optimization problems of the form <math display="block">\min_{x\in \cal{X}} f(x),</math>where <math>f:\mathcal{X}\to\R</math> denotes the objective function acting on the state space <math>\cal{X}</math>. <math>f</math> can potentially be nonconvex and nonsmooth. The algorithm isemploys basedparticles onor particlesagents to exploringexplore the state space, whilewhich communicatingcommunicate with each other to update their positions. Their dynamics follows the paradigm of [[Metaheuristic|metaheuristics]], which blend exporation with exploitation. In this sense, CBO is comparable to [[Ant colony optimization algorithms|ant colony optimization]], wind driven optimization<ref>{{Cite web |title=The Wind Driven Optimization Technique and its Application in Electromagnetics {{!}} IEEE Journals & Magazine {{!}} IEEE Xplore |url=https://ieeexplore.ieee.org/document/6407788 |access-date=2024-02-03 |website=ieeexplore.ieee.org}}</ref>, [[particle swarm optimization]] or [[Simulated annealing]]. However, compared to other heuristics, CBO was designed to have a well-posed mean-field limit
