An Improved Grey Wolf Optimizer Based on Elite Opposition-Based Learning Strategy and Non-Linear Parameters

The Grey Wolf Optimizer (GWO) has been extensively applied in meta-heuristic optimization. However, it inherently suffers from several limitations, including inaccurate solution outputs, slow convergence speed, and a high tendency to get trapped in local optima. To resolve these problems, this study introduces two targeted improvements to the GWO algorithm. Firstly, an elite opposition-based learning method is employed for initializing the grey wolf population. This method enhances the diversity of initial individuals, reinforces the algorithm’s global search ability, and accelerates convergence in the early iteration stage. Secondly, nonlinear parameters are incorporated into both the prey encircling and attacking processes of GWO. This modification expands the algorithm’s search scope during the early iterations. Ten benchmark test functions with distinct characteristics were used to validate the improved algorithm (IEN-GWO), which was compared with five well-recognized meta-heuristic algorithms. The experimental results demonstrate that IEN-GWO outperforms the compared algorithms in terms of solution precision, stability, and convergence rate.