Research on Optimization of Grey Wolf Algorithm with Multiple Improved Strategies

The Grey Wolf Optimizer (GWO) boasts remarkable optimization characteristics and is already widely applied across various domains. This paper provides an overview of the enhancements applied to the GWO, meticulously selecting nine of the most representative strategies for improvement. Subsequently, these strategies were subjected to numerical experiments using benchmark functions. The experimental results showed that: the improved grey wolf optimizer (ODGWO) with novel opposition-learning and differential mutation, exhibited superior capabilities in exploration and convergence; The strategy of amalgamating the Grey Wolf Optimizer with Particle Swarm Optimization (PSO) in the PSOGWO approach, and the integration with the Whale Optimization Algorithm (WOA) in the WOAGWO approach, notably enhanced the algorithm’s search performance. Of particular note is the fusion with the Whale Optimization Algorithm (WOAGWO), which effectively balanced the trade-off between exploration, exploitation, and the ability to evade local optima, particularly in solving fixed-dimension multimodal benchmark functions.