Traditional algorithms have been used in determining solutions for the grid scheduling problem. However, it has been witnessed in the recent times that the increased complexity and size of the job have been a reason for the degradation in algorithm's performance. This led to the emergence of a new class of heuristic algorithms that gives optimal or near-optimal solutions to optimization problems. The proposed algorithm solves multi-objective grid scheduling problem and performs further optimization. The proposed swarm-based algorithm is a threshold-constrained Ant Colony Optimization using Shortest Job First (SJF) in initialization step. The characteristics of proposed algorithm are as follows. It minimizes the job completion (execution) time and enhances resource utilization. It supports load balancing and scalability. The performance test of algorithms is carried out using a real-time dataset, which can set benchmark results for future research. Simulation results reveal that the proposed swarm algorithm obtains promising results over other algorithms taken for comparison.
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