Multi-objective task scheduling in large-scale distributed systems using a Lévy flight-based hybrid Bat-Whale optimization algorithm - Ali Mohammed Ahmed; Manar Younis Kashmola (Indonesian Journal of Electrical Engineering and Computer Science, 2026)
The rapid growth of cloud computing demands efficient task scheduling strategies capable of handling heterogeneous resources, dynamic workloads, and multiple conflicting objectives. Existing approaches often optimize a single criterion, limiting their effectiveness in large-scale distributed systems. This paper proposes hybrid Bat–Whale optimization algorithm (BWOA), a hybrid scheduling algorithm combining the Bat algorithm and Whale optimization algorithm, enhanced with Lévy flight-based exploration, adaptive crossover, and a smart local search mechanism. The framework balances global exploration and local exploitation while preserving population diversity and intensifying search around promising solutions. A problem-aware local search reallocates long-duration tasks to high performance virtual machines and selectively swaps tasks with poor response times. Experiments on a heterogeneous cloud environment with 300 tasks and 50 virtual machines, using min–max scaling for workload normalization, demonstrate that BWOA outperforms classical methods, including first come, first served (FCFS) and Min-Min scheduling algorithms, achieving superior makespan (≈32.77 s) while maintaining competitive utilization, throughput, and energy efficiency. These results highlight the effectiveness of hybrid metaheuristic approaches integrating multiple optimization strategies for multi-objective task scheduling in large scale cloud systems, providing a robust and scalable solution for both academic research and practical deployment.