Hummingbird-Inspired Modified Particle Swarm Optimization for Efficient Task Scheduling in Cloud Computing

Cloud computing delivers on-demand services and scalable computing power in near real-time, redefining modern computing paradigms. Effective task scheduling remains a critical challenge due to dynamic and heterogeneous workloads, directly influencing energy efficiency, response time, and resource utilization. The present research presents an enhanced Particle Swarm Optimization (PSO) algorithm inspired by specific hummingbird flight characteristics, chosen for their exceptional agility and efficiency. Five hummingbirdinspired concepts are integrated into PSO: incremental position updates to enhance convergence accuracy, stepwise position changes to avoid local optima, energy-conserving movements reducing computational overhead, decentralized exploration to maintain diversity, and multidirectional searches enhancing solution coverage. Comparative experiments conducted on synthetic and real-world datasets (HPC2N) with diverse task loads demonstrate measurable performance improvements, including up to 18% better resource utilization, up to a 35% decrease in imbalance degree, and up to a 20% improvement in execution cost compared to recent algorithms. These results confirm that each hummingbird-inspired concept distinctly contributes to overcoming conventional PSO limitations, significantly enhancing exploration ability, convergence speed, load balancing, and adaptability to diverse cloud computing scenarios.