A Bio-Inspired Swarm Approach to 5000-Dimensional Optimization

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...

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.