Hybrid Swarm Intelligence Models with Deep Q Learning for Dynamic Resource Allocation in Cloud Computing

Efficient and intelligent resource allocation remains a fundamental challenge in cloud computing due to the dynamic, heterogeneous, and high-dimensional nature of cloud environments. This chapter presents an adaptive hybrid optimization framework that integrates Swarm Intelligence (SI) algorithms with Deep Q Learning (DQL) to address the complexity of real-time decision-making in large-scale cloud systems. The proposed architecture leverages the global exploration capabilities of swarm-based models and the policy-learning strengths of reinforcement learning to enable adaptive and SLA-aware resource management. By combining collective search behavior with neural network-driven policy refinement, the hybrid system dynamically allocates computational resources, minimizes latency, and optimizes long-term performance under varying workload conditions. Extensive analysis demonstrates that the model efficiently navigates high-dimensional state spaces, balances exploration and exploitation, and improves convergence speed while reducing SLA violations. The chapter also includes complexity analysis and performance evaluations, confirming the scalability and responsiveness of the hybrid framework in distributed cloud infrastructures. This work contributes to the development of nextgeneration autonomous systems for cloud optimization by bridging bio-inspired computation and deep learning.