融合多策略的哈里斯鹰优化算法求解Steiner树问题OA

To address the issues of uneven population distribution,imbalanced exploration and exploita-tion phases,and susceptibility to local optima in the traditional Harris hawks optimization algorithm when solving the Steiner tree problem of graph(GSTP),an improved Harris hawks optimization algorithm in-corporating multiple strategies was proposed.Firstly,the algorithm was discretized using an S-shaped transfer function,and a Logistic-Sine hybrid chaotic mapping was introduced to optimize the population initialization process.Secondly,a dynamic adaptive weight strategy was designed to enhance the nonlin-ear expression of prey escape energy,thereby further balancing exploration and exploitation behaviors.Finally,adaptive Gaussian-Cauchy mixed mutation perturbation was applied to the optimal individuals during the later iterations to prevent the population from prematurely converging to local optima.Experi-ments were conducted on multiple GSTP instances,and the results showed that the proposed algorithm achieved higher solution accuracy and faster convergence speed.