Approximation algorithms for scheduling unrelated parallel machines
Mathematical Programming: Series A and B
Task scheduling in parallel and distributed systems
Task scheduling in parallel and distributed systems
The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Static scheduling algorithms for allocating directed task graphs to multiprocessors
ACM Computing Surveys (CSUR)
Observations on Using Genetic Algorithms for Dynamic Load-Balancing
IEEE Transactions on Parallel and Distributed Systems
Journal of Parallel and Distributed Computing
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Task Execution Time Modeling for Heterogeneous Computing Systems
HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
Grid Computing: Making the Global Infrastructure a Reality
Grid Computing: Making the Global Infrastructure a Reality
Handbook of Scheduling: Algorithms, Models, and Performance Analysis
Handbook of Scheduling: Algorithms, Models, and Performance Analysis
Parallel Metaheuristics: A New Class of Algorithms
Parallel Metaheuristics: A New Class of Algorithms
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
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
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Cellular Genetic Algorithms
Multi-objective planning for workflow execution on Grids
GRID '07 Proceedings of the 8th IEEE/ACM International Conference on Grid Computing
MOCell: A cellular genetic algorithm for multiobjective optimization
International Journal of Intelligent Systems - Special Issue on Nature Inspired Cooperative Strategies for Optimization
Comparison of Heuristics for Scheduling Independent Tasks on Heterogeneous Distributed Environments
CSO '09 Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization - Volume 01
Hybrid meta-heuristics algorithms for task assignment in heterogeneous computing systems
Computers and Operations Research
Design issues in a multiobjective cellular genetic algorithm
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Information Sciences: an International Journal
Distributed power management and control system for sustainable computing environments
GREENCOMP '10 Proceedings of the International Conference on Green Computing
Expert Systems with Applications: An International Journal
Heterogeneous computing scheduling with evolutionary algorithms
Soft Computing - A Fusion of Foundations, Methodologies and Applications
3PGCIC '11 Proceedings of the 2011 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing
A parallel micro evolutionary algorithm for heterogeneous computing and grid scheduling
Applied Soft Computing
Multiobjective scheduling of jobs with incompatible families on parallel batch machines
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
This article presents six parallel multiobjective evolutionary algorithms applied to solve the scheduling problem in distributed heterogeneous computing and grid systems. The studied evolutionary algorithms follow an explicit multiobjective approach to tackle the simultaneous optimization of a system-related (i.e. makespan) and a user-related (i.e. flowtime) objectives. Parallel models of the proposed methods are developed in order to efficiently solve the problem. The experimental analysis demonstrates that the proposed evolutionary algorithms are able to efficiently compute accurate results when solving standard and new large problem instances. The best of the proposed methods outperforms both deterministic scheduling heuristics and single-objective evolutionary methods previously applied to the problem.