'''Consensus based optimization (CBO)''' <ref>{{Cite journal |lastlast1=Pinnau |firstfirst1=René |last2=Totzeck |first2=Claudia |last3=Tse |first3=Oliver |last4=Martin |first4=Stephan |date=January 2017-01 |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=011 |pages=183–204 |doi=10.1142/S0218202517400061 |arxiv=1604.05648 |s2cid=119296432 |issn=0218-2025}}</ref> is a method 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>. The algorithm is based on particles exploring the state space, while communicating with each other to update their positions. In this sense, CBO is comparable to 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
