为解决类电磁机制（electromagnetism-like mechanism, ELM）算法在配电网故障定位问题中存在的收敛速度较慢且易陷入局部最优的问题，提出了一种改进的二进制类电磁机制（binary electromagnetism-like mechanism, BELM）算法。首先引入Sobol序列初始化种群，以保证初始种群的质量；其次，基于适应度值将种群划分为优质种群和普通种群，并针对这两个子种群分别采用最优粒子引导策略和基于XOR运算的局部搜索策略，前者引导优质粒子前往潜在最佳位置，加快收敛速度；后者通过与精英粒子的信息交流，进行全局探索和丰富种群多样性。最后通过对合力计算和粒子移动规则的改进，进一步改善算法搜索效率。仿真实验结果表明，相较于其他算法，所提算法在配电网定位故障问题中具有准确率高、收敛速度快等优点。

To address the issues of slow convergence speed and susceptibility to local optima in ELM (electromagnetism-like mechanism) algorithm for fault location problems in distribution networks, BELM (binary electromagnetism-like mechanism) algorithm was proposed. First, the Sobol sequence was introduced to initialize the population to ensure the quality of the initial population. Second, based on the fitness value, the population was divided into a high-quality population and an ordinary population, and an optimal particle guidance strategy and a local search strategy based on the XOR operation were adopted for these two sub-populations, respectively, the former guides the high-quality particles to the potentially optimal location to accelerate the convergence speed, the latter performs global exploration and enriches the diversity of the population by exchanging information with the elite particles. Finally, the search efficiency of the algorithm was further improved by the improvement of the combined force calculation and particle movement rules. The simulation results show that compared with other algorithms, the proposed algorithm demonstrates superior accuracy and rapid convergence in locating faults within distribution networks.