Abstract
Computational grids have been used to solve large scale problems in science, engineering and commerce. The task scheduling in computational grid is a complex optimization problem. Task scheduling is the fundamental issue in grid scheduling. The heuristic algorithms play a vital role in solving complex optimization problems. The distributive nature of Ant Colony Optimization (ACO) helps to find optimal or near optimal solution in efficient manner. Artificial Bee Colony Algorithm is one of the latest heuristics, which out performs classical heuristics, such as Tabu Search (TS), Simulated Annealing (SA), or even ACO. Its exploration capacity can be improved by modifying in the fitness value computation. The hybridization and the modifications in ACO and ABC improve the exploration and exploitation capability of the algorithms and enhance the convergence ability of the algorithm. The proposed hybridization of ABC algorithm with ACO algorithm reduces the waiting time, communication delay and makespan time of the schedule with good load balancing. It improves the convergence to the optimal solution. The proposed Pareto based hybrid ABC-ACO algorithm finds a population of solutions, then uses Pareto ranking to sort these solutions, and then derives the Pareto front for optimal task scheduling. The group of non-dominated solutions assists to schedule the tasks to the best available resources with a tradeoff between makespan and cost in the computational grid.