I’m excited to share my latest research on the Dynamic Hierarchical Cooperative Swarm (DHCS) algorithm, recently published on SSRN: https://papers.ssrn.com/sol3

I’m excited to share my latest research on the Dynamic Hierarchical Cooperative Swarm (DHCS) algorithm, recently published on SSRN: https://papers.ssrn.com/sol3/Delivery.cfm/7e20cab6-09bf-4fb2-a8ef-ac6538be95d6-MECA.pdf?abstractid=5593065 https://papers.ssrn.com/sol3/Delivery.cfm/7e20cab6-09bf-4fb2... DHCS is a bio-inspired metaheuristic designed for high-dimensional and complex optimization problems, addressing limitations of conventional approaches like PSO or Genetic Algorithms. Key features: Dynamic clustering & adaptive roles: Each agent autonomously decides its behavior while maintaining swarm coherence. Periodic synchronization: Ensures global coordination without sacrificing exploration. Scalability: Tested on a 5000-dimensional Ackley function with superior convergence and robustness. Efficiency: Reduces computational overhead while outperforming standard methods. Versatility: Applicable to engineering design, supply chain optimization, ML hyperparameter tuning, and financial modeling. This paper not only formalizes the DHCS framework but also presents a comprehensive experimental evaluation demonstrating its effectiveness in high-dimensional and dynamic environments. I’d love feedback from the community, especially from those working in metaheuristics, swarm intelligence, and large-scale optimization problems.