一种基于多策略融合改进的北极海雀优化算法OA

To address the problems of low convergence accuracy,slow convergence speed and sus-ceptibility to local optimization in the iterative process of Arctic Puffin Optimization(APO),this paper proposes an improved Arctic puffin optimization algorithm based on multi-strategy fusion(IAPO).The strategy of good point set is introduced in the population initialization phase.By leveraging the mathemati-cal characteristics of good point set,uniformly distributed population nodes can be generated within a spe-cific search space.Before the start of each iteration process,opposition-based learning based on the lens imaging principle is applied to recalculate the population and the optimal population after each iteration update,so as to obtain high-quality opposite solution with stronger dynamics and flexibility.The dynamic Cauchy mutation strategy is incorporated into the underwater foraging phase of the APO algorithm,and its long tail characteristics can make the mutation to produce significant perturbations,thereby avoiding the algorithm from falling into local optimization and enhancing its global search ability.In the simulation ex-periment section,IAPO is tested against APO,PSO,HBA,RBMO,POA and three other single-strategy variant algorithms are tested on the CEC2005 test function set.The results demonstrate that the compre-hensive performance of IAPO algorithm outperforms all other comparison algorithms.