An improved hawks swarm optimizer with dynamic learning

Abstract

The Harris Hawks Optimization (HHO) algorithm has recently garnered extensive attention in research and applications. However, it still faces critical challenges, including premature convergence, degradation of swarm diversity, entrapment in local optima for complex problems, difficulty balancing exploration and exploitation, and a performance heavily dependent on parameter tuning, which lacks universal guidelines. This paper proposes an improved Hawks swarm optimizer embedded with a new search strategy called BAHSO to address the challenges. The improved algorithm enhances its exploration capability while maintaining the strong exploitation characteristics by leveraging the directional perturbation mechanism inspired by the beetle antennae search (BAS) algorithm. Specifically, BAS introduces adaptive step-size adjustments and stochastic directional search to help individuals escape local optima. At the same time, a dynamic energy learning coefficient ensures balanced exploration-exploitation trade-offs throughout the hard-soft siege hunting process. Experimental results and comparisons demonstrate that BAHSO achieves superior convergence accuracy and speed, particularly in high-dimensional and multimodal landscapes, with significantly improved solution quality over the best-performing variants. Furthermore, diversity metrics confirm that BAHSO maintains higher swarm diversity in late-stage iterations than other methods, effectively mitigating stagnation issues.