基于Lévay飞行的粒子群算法在大地电磁反演中的应用

    Application of particle swarm algorithm based on Lévy flight in magnetotelluric inversion

    • 摘要: 粒子群优化算法在大地电磁测深反演中相较于一般的线性反演算法具有多种优点。然而标准粒子群算法在多维优化问题中存在早熟问题,为此,采用基于Lévy飞行随机游走策略的优化粒子群算法来处理局部最优解,增加寻优能力。通过对地电模型的反演对比表明,改进后的粒子群算法相较于标准粒子群算法适应度值下降速度更快、寻优能力更好。最后将该算法应用于已知钻孔旁实测数据,结果较好,表明该算法具有较好的实用性。

       

      Abstract: Particle swarm optimization algorithm has many advantages compared with linear inversion algorithm in magnetotelluric sounding inversion.However, the standard particle swarm algorithm also suffers from premature maturity in multidimensional optimization problems.Therefore, an optimized particle swarm algorithm based on the Lévy flight randomized wandering strategy is used to escape the local optimal solution,The results show that compared with the standard particle swarm optimization algorithm, the optimized particle swarm algorithm has faster fitness decline and better optimization ability.Finally, the improved particle swarm optimization algorithm is applied to the measured data of known boreholes, and the results show that the algorithm has good practicability.

       

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