Nature inspired meta-heuristics for grid scheduling: Single and multi-objective optimization approaches

Abstract

In this chapter, we introduce several nature inspired meta-heuristics for scheduling jobs on computational grids. Our approach is to dynamically generate an optimal schedule so as to complete the tasks in a minimum period of time as well as utilizing the resources in an efficient way. We evaluate the performance of Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony optimization (ACO) and Particle Swarm Optimization (PSO) Algorithm. Finally, the usage of Multi-objective Evolutionary Algorithm (MOEA) for two scheduling problems are also illustrated. © 2008 Springer-Verlag Berlin Heidelberg.