Hybridization of Evolutionary and Swarm Intelligence Techniques for Job Scheduling Problem

For more than a decade, scheduling of jobs has been an attractive research subject for researchers. There are several different ways to schedule jobs, and the threads which make them up. As well, the job scheduling is one of the active research fields, where the researchers work to enhance the efficiency of the job scheduling process in a scheduling environment. In existing hybrid techniques, some efficient factors related to jobs like turnaround time, job execution time and more have not been considered in the job scheduling process. The main drawback is lack of factors in the scheduling process which reduces the performance. To overcome such drawback in the existing methods, an adaptive ABC technique is proposed. In this proposed adaptive ABC technique, the term adaptiveness is achieved by using mutation, crossover and velocity in the employed bee phase for finding the new food sources. The adaptive ABC algorithm optimally allocates the jobs to the accurate processors or resources. Moreover, these existing techniques mostly concentrate on two major factors such as the minimization of the makespan and the completion time. The adaptiveness improves the efficiency of scheduling process when compared to the two conventional hybrid job scheduling techniques. The experimental result shows the performance of the proposed job scheduling process.