Improved Grey Wolf Optimization Algorithm Based on Arctangent Inertia Weight

An improved Grey Wolf Optimization (AGWO) algorithm based on arctangent inertia weight strategy is proposed to address the slow convergence speed and low optimization accuracy of the Grey Wolf Optimization (GWO) algorithm. The AGWO algorithm has non-linear characteristics and can better balance the global and local search capabilities of the algorithm. Under special situation, AGWO can degenerate into GWO, AGWO is an extent of GWO. The search performance of AGWO is tested on five classic test functions. The experimental results show that AGWO has higher optimization performance than four classical swarm intelligence algorithms.