Using evolutionary programming to schedule tasks on a suite of heterogeneous computers
Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
Journal of Parallel and Distributed Computing
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Dynamic scheduling of scientific workflow applications on the grid: a case study
Proceedings of the 2005 ACM symposium on Applied computing
Scheduling of scientific workflows in the ASKALON grid environment
ACM SIGMOD Record
Task scheduling strategies for workflow-based applications in grids
CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid (CCGrid'05) - Volume 2 - Volume 02
A Multiobjective Resources Scheduling Approach Based on Genetic Algorithms in Grid Environment
GCCW '06 Proceedings of the Fifth International Conference on Grid and Cooperative Computing Workshops
Bi-criteria Scheduling of Scientific Workflows for the Grid
CCGRID '08 Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid
Scientific Programming - Scientific Workflows
Multi-objective planning for workflow execution on Grids
GRID '07 Proceedings of the 8th IEEE/ACM International Conference on Grid Computing
NP-complete scheduling problems
Journal of Computer and System Sciences
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Grid provides global computing infrastructure for users to avail the services supported by the network. The task scheduling decision is a major concern in heterogeneous grid computing environment. The scheduling being an NP-hard problem, meta-heuristic approaches are preferred option. In order to optimize the performance of workflow execution two conflicting objectives, namely makespan (execution time) and total cost, have been considered here. In this paper, reference point based multi-objective evolutionary algorithms, R-NSGA-II and R-e-MOEA, are used to solve the workflow grid scheduling problem. The algorithms provide the preferred set of solutions simultaneously, near the multiple regions of interest that are specified by the user. To improve the diversity of solutions we used the modified form of R-NSGA-II (represented as M-R-NSGA-II). From the simulation analysis it is observed that, compared to other algorithms, R-e-MOEA delivers better convergence, uniform spacing among solutions keeping the computation time limited.