A hybrid GA-IWO scheduling algorithm

Cloud computing is the latest need of the hour network which is convenient. It is an on-demand network accessed over a shared group of computing resources. Only minimal management effort is needed to be released with no communication with a service-provider. Optimization problem such as Invasive Weed Optimization is used in cloud computing but the challenges in this technique is its premature convergence and cannot achieve global optimum, specifically for multimodal issues. In the case of Genetic Algorithm (GA), it does not always come with global optimum always, mainly the population is varied when overall solution is required. GA is a technique which is complex to be understood. To overcome these drawbacks Genetic Algorithm-Invasive Weed Optimization (GA-IWO) method is proposed. Less computation time is required in GA-IWO and it is easy to implement on embedded systems and this makes the proposed techniques beneficial in the case of real-time decision making situations. Results show that better performance can be achieved through GA-IWO.