An improved butterfly optimization algorithm in the inversion of Rayleigh wave dispersion curve
Due to the multiplicity of solutions and the multiple extrema of the inversion objection functions,conventional nonlinear opti-mization algorithms are susceptible to unstable convergence and local optimum in the inversion of Rayleigh wave dispersion curves.This study improved the standard butterfly optimization algorithm by incorporating dynamic switch probability and nonlinear self-adaptive weight factors,yielding an elevated global exploration capacity in the early stage and a high local research ability in the latter stage.Fur-thermore,the dimension-by-dimension Cauchy mutation,along with a greedy algorithm,was employed to update the current best position during each iteration,ultimately directing the whole swarm population toward the global optimum.Tests of four commonly used bench-mark functions demonstrate that the improved butterfly optimization algorithm(IBOA)outperformed other nonlinear algorithms,inclu-ding the genetic algorithm and particle swarm optimization algorithm,in terms of the global research capacity of both unimodal and mul-timodal functions.Different algorithms were adopted for the inversion of the dispersion curves of three theoretical geological models.The results show that IBOA yielded inversion results that were closer to the models even when the dispersion curves contained 10%random noise.Finally,the IBOA was applied to actual Rayleigh wave data,and the inversion results were highly consistent with the strata re-vealed by drilling.Compared with the genetic algorithm and the particle swarm optimization algorithm,the IBOA significantly improved the convergence speed,as well as solution accuracy and stability.Therefore,the IBOA has a certain practical value and application pros-pects.
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