融合反向学习和差分进化的北极海鹦优化算法OA

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.