An Improved Grey Wolf Optimization Algorithm for Solving Optimal Power Flow

Optimal power flow (OPF) remains a fundamental and challenging task in modern power systems. In this study, a modified Grey Wolf Optimizer, referred to as LMGWO, is proposed to address the OPF problem. The algorithm incorporates multiplication and division operators derived from the Arithmetic Optimization Algorithm (AOA), and integrates a lens-based opposition-based learning strategy to enhance solution diversity and avoid premature convergence. Comparative experiments with several well-known algorithms demonstrate that LMGWO exhibits superior overall performance. Results confirm that the proposed method is effective in achieving optimal solutions for OPF problems, particularly in reducing fuel costs, minimizing real power losses, and improving voltage profile quality.