Group Intelligence Optimization Algorithm of Adaptive Trigonometric Function and T-Distributed Perturbation Strategy

Grey Wolf Optimizer (GWO) is a leading swarm intelligence optimization algorithm that restores the predation behavior of gray wolves and uses their collective cooperation to achieve algorithm optimization. However, the traditional GWO algorithm has limitations regarding optimization accuracy and convergence speed. To address these problems, this paper introduces an innovative GWO variant, NTGWO, which combines adaptive trigonometric functions and T-distribution perturbation strategies. By integrating these two strategies into the GWO framework, not only the algorithm's capacity for a wide-ranging search is enhanced, the local optimization trap is avoided effectively, but also a new solution for solving complex optimization problems is provided, and the progress of swarm intelligence optimization algorithms is promoted. To verify the effectiveness of NTGWO, this paper conducted a comprehensive evaluation using 12 standard test functions and compared it to GWO, NOA, DBO, and SCA. The experimental results show that NTGWO has remarkable performance advantages.