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 - Special issue on parallel evolutionary computing
Safety and Reliability Driven Task Allocation in Distributed Systems
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
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
Journal of Parallel and Distributed Computing
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-objective scheduling algorithms for optimizing makespan and reliability on heterogeneous systems
Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures
CODES+ISSS '07 Proceedings of the 5th IEEE/ACM international conference on Hardware/software codesign and system synthesis
Reliability versus performance for critical applications
Journal of Parallel and Distributed Computing
International Journal of Knowledge-based and Intelligent Engineering Systems
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A parallel micro evolutionary algorithm for heterogeneous computing and grid scheduling
Applied Soft Computing
A novel algorithm for dynamic task scheduling
Future Generation Computer Systems
Modeling and Pareto optimization of multi-objective order scheduling problems in production planning
Computers and Industrial Engineering
Dynamic index tracking via multi-objective evolutionary algorithm
Applied Soft Computing
Computational Optimization and Applications
A hybrid metaheuristic for multiobjective unconstrained binary quadratic programming
Applied Soft Computing
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Task scheduling problem in heterogeneous systems (TSPHS) is a multiobjective optimization problem (MOP). Multiobjective evolutionary algorithms (MOEA) are well suited for solving multiobjective task scheduling problem. In this paper, the two conflicting objectives namely, makespan and reliability are considered. The performance of MOEAs can be improved by hybridization with local search. Hybridization of MOEAs improves the convergence speed to Pareto front. Simple neighborhood search (SNS) algorithm is used as the local search algorithm. The weighted-sum based approach for solving the MOP with its hybrid version is compared. Then the two MOEAs: SPEA2 and NSGA-II are compared with each other in the pure and hybrid version for random task graphs and also for a real-time numerical application graph. The simulations confirm that Hybrid NSGA-II is best suited for solving the task scheduling problem.