Improved Grey Wolf Optimization Algorithm Based on Logarithmic Power Index Inertia Weight

Grey Wolf Optimization (GWO) algorithm is an efficient optimization technique, but in some cases, it usually encounters challenges such as, optimization accuracy, unable to balance between convergence speed and avoiding falling into local optimum. To address above problems, we prosed, an improved Grey Wolf Optimization algorithm based on logarithmic power index inertia weight (LGWO), which uses the characteristics of logarithmic power function to adjust the inertia weight non-linearly, so as to enhance the algorithm's optimization efficiency. LGWO was subjected to simulations across seven standard test functions, revealing its superior convergence speed and solution accuracy in comparison to various other swarm intelligence algorithms present in the literature.