A hybrid heuristic to solve a task allocation problem
Computers and Operations Research
Journal of Parallel and Distributed Computing
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
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
Scheduling of scientific workflows in the ASKALON grid environment
ACM SIGMOD Record
Task allocation for maximizing reliability of distributed systems: a simulated annealing approach
Journal of Parallel and Distributed Computing
Multi-objective hybrid PSO using µ-fuzzy dominance
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Artificial life techniques for load balancing in computational grids
Journal of Computer and System Sciences
Scientific Programming - Scientific Workflows
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Scheduling jobs on computational grids using fuzzy particle swarm algorithm
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
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Grid computing infrastructure emerged as a next generation of high performance computing by providing availability of vast heterogenous resources. In the dynamic envirnment of grid, a schedling decision is still challenging. In this paper, we present efficient scheduling scheme for workflow grid based on discrete particle swarm optimization. We attempt to create an optimized schedule by considering two conflicting objectives, namely the execution time (makespan) and total cost, for workflow execution. Multiple solutions have been produced using non dominated sort particle swarm optimization (NSPSO) [13]. Moreover, the selection of a solution out of multiple solutions has been left to the user. The effectiveness of the used algorithm is demostrated by comparing it with well known genetic algorithm NSGA-II. Simulation analysis manifests that NSPSO is able to find set of optimal solutions with better convergence and uniform diversity in small computation overhead.