A Novel Hybrid Algorithm Integrating Grey Wolf Social Hierarchy with Evolutionary Mating

This paper proposes a novel hybrid optimization algorithm named GWEMA, which integrates the hierarchical hunting mechanism of the Grey Wolf Optimization Algorithm (GWO) with the population update strategy of the Evolutionary Mating Algorithm (EMA) to effectively solve complex optimization problems. The core innovation of the algorithm lies in the introduction of mechanisms such as gender grouping, mating index, and predator encounter. While retaining the powerful local exploitation capability of GWO, it significantly enhances the global exploration performance through the evolutionary operations of EMA. Simulation experiments show that the GWEMA algorithm can better balance the exploration and exploitation dilemma, outperforming the basic algorithms in both convergence speed and solution accuracy, and exhibiting good robustness for complex multi-modal functions. The algorithm has a clear structure and concise parameters, with strong potential for engineering applications, providing a new approach for solving complex optimization problems.