A Multi-Objective Fuzzy Evolutionary Algorithm for Job Scheduling on Computational Grids

Abstract

Scheduling jobs in grid computing is a challenging task. The job scheduling is a process of optimization of resource allocation for job completion in a optimum amount of time. There are various solutions like using dynamic programming, evolutionary algorithms etc., in literature. However, till date, no algorithm is found to be the best. This paper attempts a new job shop scheduling problem using a recent JAYA optimization algorithm. This work proposes a fuzzy based JAYA algorithm to minimize the makespan of the selected job scheduling problem. The main feature proposed is its simplicity due to the simple JAYA algorithm compared to other existing evolutionary algorithms. Experiments are conducted on four different data sets and the results are compared with other evolutionary and fuzzy based evolutionary algorithms. The proposed fuzzy based JAYA produced compatible results in terms of average makespan, flowtime and fitness.