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'''Consensus-based optimization (CBO)'''<ref name=":0">{{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>
[[File:CBORastrigin.gif|thumb|Behavior of CBO on the [[Rastrigin function]]. '''Blue:''' Particles, '''Pink:''' drift vectors and consensus point.]]
where <math>f:\mathcal{X}\to\R</math> denotes the objective function acting on the state space <math>\cal{X}</math>, which is assumed to be a [[normed vector space]]. The function <math>f</math> can potentially be nonconvex and nonsmooth. The algorithm employs particles or agents to explore the state space, which communicate with each other to update their positions. Their dynamics follows the paradigm of [[Metaheuristic|metaheuristics]], which blend
== Algorithm ==
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