Particle Swarm Optimization with Time Varying Parameters for Scheduling in Cloud Computing
Beijing Institute of Tracking and Telecommunication Technology, Beijing, 100094, China
2 The College of Postgraduate, Academy of Equipment, Beijing, 101416, China
3 Department of Information Equipment, Academy of Equipment, Beijing, 101416, China
a Corresponding author: email@example.com
Task resource management is important in cloud computing system. It's necessary to find the efficient way to optimize scheduling in cloud computing. In this paper, an optimized particle swarm optimization (PSO) algorithms with adaptive change of parameter (viz., inertial weight and acceleration coefficients) according to the evolution state evaluation is presented. This adaptation helps to avoid premature convergence and explore the search space more efficiently. Simulations are carried out to test proposed algorithm, test reveal that the algorithm can achieving significant optimization of makespan.
© Owned by the authors, published by EDP Sciences, 2015
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