计算机与现代化Issue(5)：25-42,56,92,20.DOI:10.3969/j.issn.1006-2475.2026.05.004
融合反向学习和差分进化的北极海鹦优化算法
Arctic Puffin Optimization Algorithm Integrating Opposition-based Learning and Differential Evolution
摘要
Abstract
The Arctic Puffin Optimization(APO)algorithm,proposed in 2024,is a swarm intelligence algorithm.Similar to other swarm intelligence optimization algorithms,it suffers from issues such as slow convergence speed in the early stages,being easy to fall into local optima,and insufficient balance between exploration and exploitation.To address these limitations,an im-proved APO(IAPO)algorithm incorporating multiple strategies is proposed.Firstly,a lens imaging opposition-based learning mechanism is introduced to expand the search scope,improving the efficiency of searching for the optimal solution,which en-hances the algorithm's convergence accuracy and optimization speed.Secondly,a dynamic differential evolution strategy with adaptive parameters is integrated to improve the algorithm's ability to escape local optima and achieve precise optimization.Com-parative experiments conducted on 20 benchmark test functions,as well as CEC2019 and CEC2022 test functions,demonstrate that the IAPO algorithm exhibits superior optimization performance and robustness.Finally,the IAPO algorithm is applied to solving the parameter optimization problem of Support Vector Machines(SVM),further validating its effectiveness and reliability.关键词
群智能算法/北极海鹦算法/透镜成像反向学习机制/动态差分进化策略/Wilcoxon秩和检验/SVM参数优化Key words
swarm intelligence algorithm/Arctic Puffin algorithm/lens imaging inverse learning mechanism/dynamic differen-tial evolutionary strategy/Wilcoxon rank-sum test/SVM parameter optimization分类
信息技术与安全科学引用本文复制引用
朱雅婷,汪廷华,赵宁..融合反向学习和差分进化的北极海鹦优化算法[J].计算机与现代化,2026,(5):25-42,56,92,20.基金项目
国家自然科学基金资助项目(61966002) （61966002）
江西省自然科学基金重点资助项目(20242BAB26024) （20242BAB26024）
江西省研究生创新专项资金资助项目(YC2024-S815) （YC2024-S815）
