This paper proposes Drain-Vortex Optimization (DVO), a population-based metaheuristic for continuous optimization. DVO models each candidate solution as a particle moving in a multi-drain vortex field. Its update rule decomposes motion into radial attraction toward selected drain centres and tangential rotation governed by a regularized free-vortex law. A three-phase mechanism switches between far-field exploration, spiral inward motion, and localized core exploitation according to the normalized distance to the assigned drain. The method also uses adaptive spiral exploitation, population-level vortex basin assignment, and optional stochastic basin switching to support structured diversity. DVO is evaluated against PSO, GWO, WOA, SCA, AOA, EO, and SVOA using a calibration--validation protocol. CEC 2022 is used only to select the final DVO configuration, while CEC 2017, classical functions, and five constrained engineering design problems are used for out-of-sample validation. On CEC 2017, DVO achieves the best mean $\log_{10}$ error on 34 of 58 cases and the best Friedman average rank (1.67), and is significantly better than every baseline under Holm-corrected Wilcoxon tests. On CEC 2022, DVO obtains the best Friedman rank (2.13) and is significantly better than five of the seven baselines; the differences against PSO and SVOA are not significant. DVO is less competitive on simple scalable classical functions and on small constrained engineering designs, which clarifies its operating regime. The algorithm is implemented in a vectorized GPU form that executes independent runs in parallel.

翻译：本文提出了一种名为排水涡旋优化(DVO)的群体元启发式算法，用于连续优化问题。DVO将每个候选解建模为在多排水涡流场中运动的粒子，其更新规则将运动分解为指向选定排水中心的径向吸引和由正则化自由涡旋定律控制的切向旋转。三阶段机制根据粒子与指定排水点的归一化距离，在远场探索、螺旋向内运动和局部核心开发之间切换。该方法还采用自适应螺旋开发、群体级涡旋流域分配以及可选的随机流域切换以支持结构化多样性。采用校准-验证协议将DVO与PSO、GWO、WOA、SCA、AOA、EO和SVOA算法进行对比评估。仅使用CEC 2022函数集选择最终DVO配置，而CEC 2017函数集、经典函数以及五个约束工程设计问题用于样本外验证。在CEC 2017函数集上，DVO在58个案例中的34个取得最佳平均$\log_{10}$误差，并获得最佳Friedman平均排名(1.67)，且在Holm校正Wilcoxon检验下显著优于所有基线算法。在CEC 2022函数集上，DVO获得最佳Friedman排名(2.13)，且显著优于七个基线算法中的五个；与PSO和SVOA的差异不显著。DVO在简单可扩展经典函数和小规模约束工程设计问题上竞争力较弱，这明确界定了其适用工况。该算法以向量化GPU形式实现，可并行执行独立运行。