Abstract
This paper proposes an enhanced Particle Swarm Optimization (PSO) algorithm in order to deal with the issue that the time and cost of the PSO algorithm is quite high when scheduling workflow tasks in a cloud computing environment. To reduce particle dimensions and ensure initial particle quality, intensive tasks are combined when scheduling workflow tasks. Next, the particle initialization is optimized to ensure better initial particle quality and reduced search space. Then, a suitable self-adaptive function is integrated to determine the best direction of the particles. The experiments show that the proposed enhanced PSO algorithm has better convergence speed and better performance in the execution of workflow tasks.
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