HGWWO: A Hybrid Grey Wolf–Whale Optimizer for Load Balancing in Cloud Computing Environments

This paper aims to develop an efficient and adaptive load balancing algorithm for cloud computing environments using a novel hybrid meta-heuristic approach. Effective load balancing is necessary for optimum performance and resource utilization in cloud computing systems. Most conventional meta-heuristic algorithms suffer from premature convergence and poor exploration–exploitation tradeoffs. An innovative hybrid meta-heuristic algorithm, Hybrid Grey Wolf–Whale Optimizer (HGWWO), is proposed for efficiently and dynamically balancing cloud load. HGWWO integrates the leadership hierarchy and adaptive hunting strategy of the Grey Wolf Optimizer (GWO) with the spiral-shaped exploitation mechanism of the Whale Optimization Algorithm (WOA), resulting in high convergence rates. The algorithm is implemented in a multi-objective cloud load balancing model to reduce response time, energy usage, and makespan while optimizing resource utilization among virtual machines. The experimental outcomes prove that HGWWO outperforms existing algorithms regarding throughput, waiting time, and execution efficiency. The suggested model has potential for real-time cloud scheduling of resources and is an efficient solution for scalable and heterogeneous cloud environments.