Hybrid Adaptive Butterfly Optimization and Genetic Algorithm for optimized task scheduling

In cloud computing, task scheduling is an integral challenge in computational environments, owing to the difficulty in balancing resource utilization, makespan and energy efficiency. The present work recommends a hybrid optimization algorithm that associates the Adaptive Butterfly Optimization Algorithm (ABOA) and Genetic Algorithm (GA) for competent task scheduling. To subdue the restrictions of standalone algorithms, hybrid ABOA-GA framework uses the global exploration capabilities of ABOA and the local refinement strengths of GA. The obtained results have been affirmed through simulations across varying task sets and configurations, benchmarking its performance against standalone ABOA, standalone GA, and traditional scheduling algorithms, such as Round Robin (RR) and First Come First Serve (FCFS). The experimental results showed that the proposed approach attains substantial enhancements in resource utilization, makespan reduction and energy efficiency. Precisely, the hybrid algorithm reduces makespan by 12%, increases resource utilization by 10%, and enhances energy efficiency by 15% compared to other algorithms. Additionally, adaptive nature of BOA ensures optimal task-resource allocation, even under heterogeneous task configurations.