For transient electromagnetic inversion, a gradient‐based algorithm is strongly dependent on the quality of the initial model, while any non‐gradient‐based algorithm often falls too easily into local optima. This paper proposes a joint differential‐evolution–particle‐swarm‐optimization inversion algorithm, which provides a better global optimization. A dual‐population evolution strategy and information exchange mechanism is presented. For verification, this is followed by adoption of a layered inversion model in the transient electromagnetic inversion with a central loop. The results show that the differential‐evolution–particle‐swarm‐optimization joint algorithm can reduce the probability of a premature phenomenon (i.e. falling into local optima) and improve the inversion accuracy, efficiency and stability, with a fast convergence occuring in the early stages. Furthermore, the proposed algorithm has a higher degree of fitting (prediction ability) for data inversion and is feasible for transient electromagnetic inversion.

中文翻译：

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基于粒子群优化和差分进化算法的瞬态电磁反演

对于瞬态电磁反演，基于梯度的算法在很大程度上取决于初始模型的质量，而任何基于非梯度的算法通常都太容易陷入局部最优。本文提出了一种联合的差分-进化-粒子群优化优化反演算法，它提供了更好的全局优化。提出了双重种群演化策略和信息交换机制。为了进行验证，随后在带有中央回路的瞬变电磁反演中采用分层反演模型。结果表明，微分进化粒子群优化联合算法可以降低过早现象（即陷入局部最优）的概率，提高反演精度，效率和稳定性，在早期阶段发生快速收敛。此外，该算法对数据反演具有较高的拟合度（预测能力），对于瞬态电磁反演是可行的。