Particle Swarm Optimization (PSO) is susceptible to premature convergence when the swarm collapses around the global best, particularly on multimodal landscapes in higher dimensions. We propose Divergence-guided PSO (DPSO), which augments the velocity update with a modulation term that repels particles whose personal bests have converged near the global best. The repulsion is gated by a Gaussian similarity kernel, which we prove is equivalent to an exponentially decaying function of the KL divergence between Gaussian-embedded personal and global bests, connecting the mechanism to the family of $f$-divergences and providing a principled basis for kernel design. Experiments on 36 benchmark functions (15 unimodal, 21 multimodal) across dimensions $D \in \{10, 30, 50\}$, each with 30 independent runs, show that DPSO frequently outperforms standard PSO on multimodal problems, with improvements of 2-8$\times$ on functions such as Pinter, Ackley, and Levy, and up to 5$\times$ reduction in run-to-run variance. On unimodal landscapes the modulation term is counterproductive, confirming that DPSO targets the exploration-exploitation trade-off rather than offering a universal improvement. The method adds one hyperparameter, incurs 15--25\% wall-clock overhead, and does not increase the asymptotic per-iteration complexity of PSO. The project code is available here: https://github.com/Kleyt0n/dpso

翻译：[translated abstract in Chinese] 粒子群优化在群体围绕全局最优解收敛时容易陷入早熟收敛，特别是在高维多峰地形上。我们提出散度引导的粒子群优化，该算法在速度更新中引入一个调制项，用于排斥那些个体最优解已收敛至全局最优解附近的粒子。排斥作用由一个高斯相似性核门控，我们证明该核等价于高斯嵌入的个体最优解与全局最优解之间KL散度的指数衰减函数，从而将该机制与$f$-散度族建立联系，并为核设计提供了理论基础。在维度$D \in \{10, 30, 50\}$的36个基准函数（15个单峰、21个多峰）上进行的实验（每个函数独立运行30次）表明，散度引导的粒子群优化在多峰问题上通常优于标准粒子群优化，在Pinter、Ackley和Levy等函数上性能提升2-8倍，运行间方差最多降低5倍。在单峰地形上，该调制项会产生反效果，证实散度引导的粒子群优化针对的是探索-利用权衡问题，而非提供普适性改进。该方法仅增加一个超参数，带来15-25%的时钟开销，且不增加粒子群优化每轮迭代的渐近复杂度。项目代码可在此处获取：https://github.com/Kleyt0n/dpso