Improved Grey Wolf Optimization Algorithm Based on Logarithmic Inertia Weight

To solve the problem that the convergence speed of Grey Wolf Optimization (GWO) is not fast enough and tends to fall into local optimization, the improved Grey Wolf Optimization based on logarithmic inertia weight (LGWO) is proposed. LGWO utilizes the characteristics of logarithmic function to realize the nonlinear adjustment of inertia weight, thus better balancing the global exploration and local mining capabilities of the GWO. Meanwhile, the logarithmic inertia weight strategy is introduced into the location update of the GWO to deal with the location update process of grey wolves, which reduces the possibility of the algorithm falling into local convergence and accelerates the convergence speed. Five classical test functions are used to test the optimization performance of LGWO. Compared with the existing swarm intelligence algorithm, LGWO accelerates the convergence speed and improves the convergence accuracy and stability of the GWO.