A novel DE/VS hybrid algorithm for enhanced optimization in numerical and engineering problems

Abstract

Effectively balancing exploration and exploitation is crucial for metaheuristic algorithms to achieve high-quality solutions in complex search spaces. The proposed DE/VS hybrid algorithm combines the strengths of differential evolution (DE) and vortex search (VS) to enhance global optimization performance. DE provides robust exploration but struggles with exploitation, while VS excels in exploitation but lacks exploration, often leading to premature convergence. The DE/VS framework introduces a hierarchical subpopulation structure and dynamic population size adjustment, ensuring a balanced trade-off between exploration and exploitation. This adaptive mechanism enhances convergence efficiency and prevents stagnation. Experimental evaluations across benchmark functions and engineering problems confirm that DE/VS consistently outperforms traditional methods. Statistical analysis further validates its superiority, demonstrating its effectiveness in solving complex optimization problems.