基于随机增强量子粒子群算法的弹性波数值模拟

Numerical modeling of elastic waves using the random-enhanced QPSO algorithm

在本文中,我们提出了一种随机增强量子粒子群优化算法,并基于该随机增强量子粒子群算法提出了一种新的有限差分格式。随机增强量子粒子群优化算法具有明显的收敛速度优势,可以在第200代内收敛。在相同条件下,未改进的量子粒子群算法的收敛速度远低于随机增强量子粒子群算法。数值频散分析表明,基于随机增强量子粒子群算法的优化有限差分格式具有更大的频谱覆盖范围并将精度误差控制在了有效范围之内,这意味着随机增强量子粒子群算法具有更好的搜索全局精确解的能力。最后,采用基于随机增强量子粒子群算法的优化有限差分格式对弹性波动方程进行数值模拟。数值模拟结果表明,基于随机增强量子粒子群算法的优化有限差分格式能有效压制数值频散。

In this paper,we derive a random-enhanced quantum particle swarm optimization(QPSO)algorithm and develop a new finite difference(FD)scheme based on this algorithm.The random-enhanced QPSO algorithm has advantages of convergence speed and can converge within the 200th iteration.Under the same conditions,the convergence speed of the conventional QPSO algorithm is much lower than that of the random-enhanced QPSO algorithm.Numerical dispersion analysis reveals that the optimized FD scheme based on the random-enhanced QPSO algorithm has a broader spectral coverage,and the accuracy error is maintained within a valid range,signifying that the random-enhanced QPSO algorithm can better search for accurate global solutions.Finally,numerical modeling of elastic wave equations is performed using the optimized FD scheme based on the random-enhanced QPSO algorithm.The numerical modeling results indicate that the optimized FD scheme based on the random-enhanced QPSO algorithm can effectively suppress numerical dispersion.