Cloud computing is on-demand Internet-based computing, which is a highly scalable service adopted by different working and non-working classes of people around the globe. Task scheduling one of the critical applications used by end-users and cloud service providers. The significant challenging in the task scheduler is to find an optimal resource for the given input task. In this paper, we proposed Hybrid Electro Search with a genetic algorithm (HESGA) to improve the behavior of task scheduling by considering parameters such as makespan, load balancing, utilization of resources, and cost of the multi-cloud. The proposed method combined the advantage of a genetic algorithm and an electro search algorithm. The genetic algorithm provides the best local optimal solutions, whereas the Electro search algorithm provides the best global optima solutions. The proposed algorithm outperforms than existing scheduling algorithms such as Hybrid Particle Swarm Optimization Genetic Algorithm (HPSOGA), GA, ES, and ACO.

云计算是基于Internet的按需计算，这是一种高度可扩展的服务，被全球不同的上班族和非上班族采用。任务调度最终用户和云服务提供商使用的关键应用程序之一。任务计划程序中的重大挑战是为给定的输入任务找到最佳资源。在本文中，我们提出了一种带有遗传算法（HESGA）的混合电子搜索，以通过考虑诸如工期，负载平衡，资源利用和多云成本等参数来改善任务调度的行为。所提出的方法结合了遗传算法和电子搜索算法的优点。遗传算法提供了最佳的局部最优解，而电子搜索算法提供了最佳的全局最优解。