Inversion of Rayleigh wave dispersion curves based on the improved sparrow search algorithm
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摘要: 非线性优化算法在给定的参数搜索范围内对最优解进行全局搜索,在全局搜索方面具有先天的优势,具有一定的跳出局部极值的能力。本文将一种新兴的非线性优化算法——麻雀搜索算法引入瑞利波频散曲线反演问题,针对频散曲线反演问题瑞利波频散曲线反演问题多参数、多局部极值的特点,引入自适应t分布对算法进行改进。三种理论模型的反演实验数据表明,改进的麻雀搜索算法与传统麻雀搜索算法相比具有更好的反演精度和稳定性,同时具有较好的抗随机噪声的能力。与粒子群算法和差分进化算法两种较成熟的非线性优化算法进行对比,改进的麻雀搜索算法较好地平衡了迭代前期的全局搜索和迭代后期的局部搜索,取得了与粒子群算法和差分进化算法相比更好的效果。Abstract: Nonlinear optimization algorithms can be used to conduct a global search for the optimal solutions within a given parameter range, inherently making them highly competent in performing a global search and escaping from local extrema.In this study,an emerging nonlinear optimization algorithm-the sparrow search algorithm (SSA) was introduced for the inversion of Rayleigh wave dispersion curves.To address the problems of multiple parameters and local extrema, adaptive t-distribution was introduced.The data acquired from the inversion experiment of three theoretical models indicate that the improved SSA has high inversion accuracy,stability,and resistance to random noise compared with the conventional SSA.Furthermore,the improved SSA can yield better performance than particle swarm optimization and differential evolution algorithm due to its capability to achieve a more reasonable balance between the early global search and late local search in the process of iteration.
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