Comparative Analysis of Convergence and Performance of Improved Northern Goshawk Optimization Algorithm

In order to solve the problems of the northern goshawk optimization (NGO) algorithm, which quickly falls into local optimal, an improved northern goshawk optimization (INGO) algorithm is proposed in this paper. Firstly, during the population initialization stage, the good point set method is introduced to map to the search space, improving the population??s diversity and avoiding precociousness. In the position update stage, the osprey local exploration position update strategy and adaptive inertia weight factor are added to enhance global exploration and local development capabilities and improve the convergence speed and accuracy of the algorithm. Secondly, the Markov chain model of the hunting process of the northern goshawk, based on the INGO algorithm, is established to prove the global convergence. The effectiveness of the INGO algorithm is verified through experimental simulation and comparative analysis with six classical intelligent algorithms. The INGO algorithm??s convergence curve and Wilcoxon rank sum test analysis are carried out. Experimental results show that the INGO algorithm can effectively avoid falling into local optimality and has vital convergence accuracy and robustness. Finally, in order to further characterize the practical application capability of the INGO algorithm, the algorithm is successfully applied to engineering design problems to verify the effectiveness of the INGO algorithm in practical applications.