Multiobjective evolutionary computation algorithms for solving task scheduling problem on heterogeneous systems

The task scheduling problem in heterogeneous systems (TSPHS) is a NP-complete problem. It is a multiobjective optimization problem (MOP). The objectives such as makespan, average flow time, robustness and reliability of the schedule are considered for solving task scheduling problem. This paper considers only the two objectives of minimizing the makespan (schedule length) and maximizing the reliability in the multiobjective task scheduling problem. Multiobjective Evolutionary Computation algorithms (MOEAs) are well suited for Multiobjective task scheduling for heterogeneous environment. The two Multi-Objective Evolutionary Algorithms such as Multiobjective Genetic Algorithm (MOGA) and Multiobjective Evolutionary Programming (MOEP) with non-dominated sorting are developed and compared for the various random task graphs and also for a real-time numerical application graph. This paper demonstrates the capabilities of MOEAs to generate well-distributed Pareto optimal fronts in a single run.