LEVERAGING BIO-INSPIRED ALGORITHMS TO ENHANCE EFFICIENCY IN COVID-19 VACCINE DISTRIBUTION

The Covid-19 pandemic has created unprecedented challenges for global vaccine distribution, highlighting the critical need for efficient logistical strategies that ensure timely delivery and equitable access to vaccines. This study examines the application of bio-inspired algorithms—Ant Colony Optimization (ACO), Genetic Algorithms (GAs), and Particle Swarm Optimization (PSO)—to optimize the distribution process of Covid-19 vaccines. By conducting a comparative analysis, the research evaluates each algorithm's effectiveness in minimizing total delivery time, reducing distribution costs, and improving convergence speed.The findings reveal that Genetic Algorithms (GAs) provide the most comprehensive optimization solution, outperforming other methods in both delivery time and cost-efficiency. GAs demonstrated a superior ability to navigate complex logistical networks, making them highly effective for large-scale vaccine distribution efforts. In contrast, Ant Colony Optimization (ACO) emerged as the fastest algorithm in terms of convergence speed, rapidly identifying optimal distribution routes. This makes ACO particularly valuable in scenarios where quick decision-making is crucial, though it may require additional refinement to match the cost-effectiveness of GAs.Particle Swarm Optimization (PSO), while not leading in any single category, offers a well-balanced performance across all metrics, proving to be a reliable option for consistent and adaptable optimization. PSO's ability to balance trade-offs between delivery speed, cost, and algorithmic efficiency makes it a practical choice for real-world applications where multiple factors must be simultaneously optimized.The study also explores the potential of hybrid approaches, which combine the strengths of different bio-inspired algorithms to achieve superior results. Additionally, the integration of adaptive algorithms and machine learning techniques is identified as a promising avenue for further enhancing vaccine distribution strategies, enabling systems to dynamically adjust to changing conditions and demands.The research concludes with several recommendations for future exploration. Emphasizing the importance of scalability, it suggests that bio-inspired algorithms should be adapted for larger, more complex distribution networks. The study also advocates for the real-time implementation of these algorithms, allowing for more responsive and efficient vaccine logistics. Finally, the paper highlights the need for multi-objective optimization approaches that can address the diverse and evolving challenges associated with global vaccine distribution, ultimately contributing to more resilient and equitable public health outcomes.