Cloud computing provides a number of resources over the internet to the users based on their request. These resources need to be scheduled in an efficient manner so that not only the provider gets benefited out of it, but the user also can take its advantage to the full extent. Therefore, resource scheduling is a critical and demanding requirement in a cloud environment. In this paper, we are proposing a bio-inspired approach, in which we have modified the existing particle swarm optimization (PSO) Algorithm and have combined it with genetic algorithm (GA) which in turn has the features and advantages of both the approaches. The proposed inventive particle swarm optimization with genetic algorithm (IPSO-GA) not only schedules resources efficiently, but also effectively manage the resources. The proposed approach is compared with traditional approaches on CloudSim simulator, where the proposed algorithm outperforms the traditional algorithms in terms of makespan time, execution time and resource utilization. Our proposed approach IPSO-GA has given better results than the existing approaches.

云计算根据用户的请求通过互联网向用户提供大量资源。这些资源需要以有效的方式进行调度，这样不仅提供商可以从中受益，而且用户也可以充分利用其优势。因此，资源调度是云环境中一项关键且苛刻的要求。在本文中，我们提出了一种仿生方法，其中我们修改了现有的粒子群优化 (PSO) 算法，并将其与遗传算法 (GA) 相结合，遗传算法 (GA) 又具有两种方法的特点和优点。所提出的创造性粒子群优化与遗传算法 (IPSO-GA) 不仅有效地调度资源，而且有效地管理资源。将所提出的方法与 CloudSim 模拟器上的传统方法进行比较，所提出的算法在制造时间、执行时间和资源利用率方面优于传统算法。我们提出的 IPSO-GA 方法比现有方法给出了更好的结果。