Assigning tasks efficiently in cloud computing is a challenging problem and is considered an NP-hard problem. Many researchers have used metaheuristic algorithms to solve it, but these often struggle to handle dynamic workloads and explore all possible options effectively. Therefore, this paper presents a new hybrid method that combines two popular algorithms, Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO). GWO offers strong global search capabilities (exploration), while PSO enhances local refinement (exploitation). The hybrid approach, called HybridPSOGWO, is compared with other existing methods like MPSOSA, RL-GWO, CCGP, and HybridPSOMinMin, using key performance indicators such as makespan, throughput, and load balancing. We tested our approach using both a simulation tool (CloudSim Plus) and real-world data. The results show that HybridPSOGWO outperforms other methods, with up to 15\% improvement in makespan and 10\% better throughput, while also distributing tasks more evenly across virtual machines. Our implementation achieves consistent convergence within a few iterations, highlighting its potential for efficient and adaptive cloud scheduling.

翻译：在云计算中高效分配任务是一个具有挑战性的问题，且被视为NP难问题。许多研究者已使用元启发式算法来解决该问题，但这些算法往往难以有效处理动态工作负载并充分探索所有可能选项。因此，本文提出一种新的混合方法，结合了两种流行算法——灰狼优化算法（GWO）与粒子群优化算法（PSO）。GWO提供了强大的全局搜索能力（探索），而PSO则增强了局部精细化搜索（利用）。该混合方法（称为HybridPSOGWO）与MPSOSA、RL-GWO、CCGP及HybridPSOMinMin等现有方法进行了对比，评估指标包括完工时间、吞吐量和负载均衡。我们使用仿真工具（CloudSim Plus）和真实数据对该方法进行了测试。结果表明，HybridPSOGWO在性能上优于其他方法，完工时间最多可缩短15%，吞吐量提升10%，同时能在虚拟机间更均衡地分配任务。我们的实现方案在数次迭代内即可实现稳定收敛，突显了其在高效自适应云调度方面的潜力。