文献类型：期刊文献

英文题名：Stratospheric vortex optimization algorithm: a new meta-heuristic algorithm, applied to continuous optimization and engineering applications

作者：He, Haiquan Huang, Haisong Yuan, Sen Zhao, Yongpeng

第一作者：He, Haiquan

通信作者：Yuan, S[1]

机构：[1]Guizhou Univ, Key Lab Adv Mfg Technol, Minist Educ, Guiyang 550025, Guizhou, Peoples R China;[2]Chongqing Vocat Tech Univ Mechatron, Informat Engn Inst, Chongqing 402760, Peoples R China;[3]Guizhou Equipment Mfg Digital Workshop Modeling &, Guiyang 550025, Guizhou, Peoples R China;[4]Guizhou Inst Technol, Sch Mech Engn, Guiyang 550003, Guizhou, Peoples R China

第一机构：Guizhou Univ, Key Lab Adv Mfg Technol, Minist Educ, Guiyang 550025, Guizhou, Peoples R China

通信机构：corresponding author), Guizhou Inst Technol, Sch Mech Engn, Guiyang 550003, Guizhou, Peoples R China.|贵州理工学院机械工程学院;贵州理工学院;

年份：2026

卷号：29

期号：3

外文期刊名：CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS

收录：;EI(收录号：20260820134945);WOS:【SCI-EXPANDED(收录号:WOS:001696320800014)】；

基金：This work was supported by the National Natural Science Foundation of China (52165063); Guizhou Provincial Science and Technology Projects (GCC [2022]0.006 - 1); (Qiankehe support normal [2022] No.165, [2021] No.445, [2021] No.172, [2021] No.397, [2022] No.008)]; the Natural Science Foundation of Chongqing (CSTB2022NSCQ-MSX1600); the Science and Technology Platform and Talent Team Building Plan Project of Guizhou Province [2023] No. 134; Higher Education Engineering Research Center of Guizhou Province (Grant No. QJJ[2023]040); Research on Mechanism Analysis of Cutting Chatter and Adaptive Control of Machining Stability for Thin walled Parts of Difficult-to-cut Materials(2023GCC033); Research on Key Technologies and Equipment for Intelligent Rapid Excavation in Coal Mines under Complex Conditions in Southwest China(Grant No. QJJ[2025]003).

语种：英文

外文关键词：Metaheuristic optimization; Stratospheric vortex optimization algorithm; Potential vorticity; Rossby wave; Sudden stratospheric warming; Engineering design

摘要：Nature-inspired metaheuristics are powerful for complex optimization yet often suffer from exploration-exploitation imbalance in nonlinear and high-dimensional settings. We propose the Stratospheric Vortex Optimization Algorithm (SVOA), which introduces a dynamics-informed tri-mechanism by coupling PV-gradient-driven radial convergence (intensified local search), Rossby-like spiral motion (enhanced global exploration), and a heavy-tailed SSW perturbation (adaptive diversification), coordinated via adaptive control. Comprehensive evaluations on CEC2017, CEC2020, and CEC2022 show consistent state-of-the-art performance: on CEC2017 (29 functions, 10/30/50/100D) SVOA achieves an average Friedman rank of 2.81 (overall 1st), outperforming GWO (7.06) and AVOA (7.61), with particularly strong 30D performance (1.79); on CEC2020 (10/20D) it attains 3.10 (overall 1st) ahead of DMO (7.05); on CEC2022 (10/20D) it reaches 3.08 (overall 1st) surpassing DMO (6.65). These correspond to approximate to 54-63% lower average ranks than leading competitors. Wilcoxon tests (alpha = 0.05) confirm significance, with SVOA outperforming GWO on 17-18 high-dimensional CEC2017 problems. Engineering validations further demonstrate practicality: SVOA achieves best or near-best solutions in four classic designs-spring (1.27 & times; 10(-)& sup2;), pressure vessel (5.885 & times; 10 & sup3;), tubular column (2.65 & times; 10 & sup1;), and rolling bearing (- 2.43 & times; 10(5))-while outperforming 36 state-of-the-art algorithms. These results evidence SVOA's superior stability, scalability (up to 100D), and accuracy, and establish the PV-Rossby-SSW framework as a rigorous, interpretable paradigm for atmospheric-inspired metaheuristic design and complex engineering optimization.