Task scheduling is a critical research challenge in cloud computing, a transformative technology widely adopted across industries. Although numerous scheduling solutions exist, they predominantly optimize singular or limited metrics such as execution time or resource utilization often neglecting the need for comprehensive multi-objective optimization. To bridge this gap, this paper proposes the Pareto-based Hybrid Whale-Seagull Optimization Algorithm (PHWSOA). This algorithm synergistically combines the strengths of the Whale Optimization Algorithm (WOA) and the Seagull Optimization Algorithm (SOA), specifically mitigating WOA's limitations in local exploitation and SOA's constraints in global exploration. Leveraging Pareto dominance principles, PHWSOA simultaneously optimizes three key objectives: makespan, virtual machine (VM) load balancing, and economic cost. Key enhancements include: Halton sequence initialization for superior population diversity, a Pareto-guided mutation mechanism to avert premature convergence, and parallel processing for accelerated convergence. Furthermore, a dynamic VM load redistribution mechanism is integrated to improve load balancing during task execution. Extensive experiments conducted on the CloudSim simulator, utilizing real-world workload traces from NASA-iPSC and HPC2N, demonstrate that PHWSOA delivers substantial performance gains. Specifically, it achieves up to a 72.1% reduction in makespan, a 36.8% improvement in VM load balancing, and 23.5% cost savings. These results substantially outperform baseline methods including WOA, GA, PEWOA, and GCWOA underscoring PHWSOA's strong potential for enabling efficient resource management in practical cloud environments.

翻译：任务调度是云计算领域的一项关键研究挑战，云计算作为一种变革性技术已在各行业广泛应用。尽管现有多种调度方案，但它们主要优化单一或有限指标，如执行时间或资源利用率，往往忽视了全面多目标优化的需求。为弥补这一不足，本文提出了基于帕累托的混合鲸鱼-海鸥优化算法（PHWSOA）。该算法协同结合了鲸鱼优化算法（WOA）和海鸥优化算法（SOA）的优势，特别针对WOA在局部开发能力上的局限以及SOA在全局探索方面的约束进行了改进。通过利用帕累托支配原理，PHWSOA同时优化三个关键目标：完工时间、虚拟机（VM）负载均衡以及经济成本。关键改进包括：采用Halton序列初始化以提升种群多样性，引入帕累托引导的变异机制以避免早熟收敛，并采用并行处理以加速收敛。此外，算法集成了动态虚拟机负载再分配机制，以改善任务执行期间的负载均衡。在CloudSim模拟器上进行的广泛实验，使用来自NASA-iPSC和HPC2N的真实工作负载轨迹，表明PHWSOA实现了显著的性能提升。具体而言，该算法将完工时间最多降低72.1%，虚拟机负载均衡改善36.8%，并节省23.5%的成本。这些结果显著优于包括WOA、遗传算法（GA）、PEWOA和GCWOA在内的基线方法，凸显了PHWSOA在实际云环境中实现高效资源管理的强大潜力。