The widespread usage of cloud computing in different fields causes many challenges as resource scheduling, load balancing, power consumption, and security. To achieve a high performance for cloud resources, an effective scheduling algorithm is necessary to distribute jobs among available resources in such a way that maintain the system balance and user tasks are responded to quickly. This paper tackles the multi-objective scheduling problem and presents a modified Harris hawks optimizer (HHO), called elite learning Harris hawks optimizer (ELHHO), for multi-objective scheduling problem. The modifications are done by using a scientific intelligent method called elite opposition-based learning to enhance the quality of the exploration phase of the standard HHO algorithm. Farther, the minimum completion time algorithm is used as an initial phase to obtain a determined initial solution, rather than a random solution in each running time, to avoid local optimality and satisfy the quality of service in terms of minimizing schedule length, execution cost and maximizing resource utilization. The proposed ELHHO is implemented in the CloudSim toolkit and evaluated by considering real data sets. The obtained results indicate that the presented ELHHO approach achieves results better than that obtained by other algorithms. Further, it enhances performance of the conventional HHO.

云计算在不同领域的广泛应用，带来了资源调度、负载均衡、功耗、安全等诸多挑战。为了实现云资源的高性能，需要一种有效的调度算法在可用资源之间分配作业，以保持系统平衡和快速响应用户任务。本文解决了多目标调度问题，并针对多目标调度问题提出了一种改进的 Harris hawks 优化器 (HHO)，称为精英学习 Harris hawks 优化器 (ELHHO)。这些修改是通过使用一种称为精英对立学习的科学智能方法来完成的，以提高标准 HHO 算法的探索阶段的质量。更远，以最小完成时间算法为初始阶段，获得确定的初始解，而不是在每个运行时间随机解，避免局部最优，满足服务质量在最小化调度长度、执行成本和最大化资源方面利用率。提议的 ELHHO 在 CloudSim 工具包中实现，并通过考虑真实数据集进行评估。获得的结果表明，所提出的 ELHHO 方法比其他算法获得的结果更好。此外，它增强了传统 HHO 的性能。提议的 ELHHO 在 CloudSim 工具包中实现，并通过考虑真实数据集进行评估。获得的结果表明，所提出的 ELHHO 方法比其他算法获得的结果更好。此外，它增强了传统 HHO 的性能。提议的 ELHHO 在 CloudSim 工具包中实现，并通过考虑真实数据集进行评估。获得的结果表明，所提出的 ELHHO 方法比其他算法获得的结果更好。此外，它增强了传统 HHO 的性能。