Consensus-based global optimization with personal best

Consensus-based global optimization with personal best

In this paper we propose a variant of a consensus-based global optimization (CBO) method that uses personal best information in order to compute the global minimum of a non-convex, locally Lipschitz continuous function. The proposed approach is motivated by the original particle swarming algorithms,...

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Bibliographic Details
Main Authors: Claudia Totzeck, Marie-Therese Wolfram
Format: Article
Language:English
Published: AIMS Press 2020-09-01
Series:Mathematical Biosciences and Engineering
Subjects:
global optimization
interacting particle systems
consensus formation
stochastic differential equations
personal best information
non-convex optimization
Online Access:https://www.aimspress.com/article/doi/10.3934/mbe.2020320?viewType=HTML

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https://www.aimspress.com/article/doi/10.3934/mbe.2020320?viewType=HTML

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