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Consensus-based optimization via jump-diffusion stochastic differential equations
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DOI：10.1142/S0218202523500082.png)
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We introduce a new consensus-based optimization (CBO) method where an interacting particle system is driven by jump-diffusion stochastic differential equations (SDEs). We study well-posedness of the particle system as well as of its mean-field limit. The major contributions of this paper are proofs of convergence of the interacting particle system towards the mean-field limit and convergence of a discretized particle system towards the continuous-time dynamics in the mean-square sense. We also prove convergence of the mean-field jump-diffusion SDEs towards global minimizer for a large class of objective functions. We demonstrate improved performance of the proposed CBO method over earlier CBO methods in numerical simulations on benchmark objective functions.
Keyword:
Global non-convex optimization
interacting particle systems
mean-field jump-diffusion SDEs
McKean-Vlasov SDEs with jumps
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3
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2.2K
被引数:
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