Hybrid Ant Colony-Particle Swarm Optimization for Dynamic Resource Allocation in Cloud Data Centers
DOI:
https://doi.org/10.29304/jqcsm.2025.17.32378Keywords:
Ant Colony algorithm, Particle Swarm OptimizationAbstract
Effective use of computational resources is a very challenging issue in cloud data centres as demands from users are very high. However, classical optimization methods are often not able to cope with changing workloads, which means they can yield to inefficient decisions. A Hybrid Optimization Algorithm based on PSO Ant Colony algorithm hybrid PSO–ACO is presented in this paper for the purpose of optimizing resource allocation efficiency in cloud environment. In this hybrid model, the heuristic search ability of ACO and exploitative nature of PSO is synergized to deliver the best heuristics to meet the demands of dynamic resource provisioning with minimum energy consumption, reduced SLA violation and improved load balancing. The results supported that the hybrid PSO–ACO algorithm achieves the highest resource efficiency with reduces execution time and SLA violations, balances load effectively and reaches optimal solutions quickly and stably and this means that the hybrid ACO-PSO approach clearly outperforms both ACO and PSO individually in all performance indicators, making it the best choice for dynamic cloud computing systems.
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Copyright (c) 2025 Hiba Abdulrazzak Ahmed
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