The computing method of the average optimal position is one of the most
important factors that affect the optimization performance of the QPSO
algorithm. Therefore, a particle position weight computing method based on
particle fitness value grading is proposed, which is called HWQPSO
(hierarchical weight QPSO). In this method, the higher the fitness value of a
particle, the higher the level of the particle, and the greater the weight.
Particles at different levels have different weights, while particles at the
same level have the same weight. Through this method, the excellent particles
have higher average optimal position weight, and at the same time, the
absolute weight of a few particles is avoided, so that the algorithm can
quickly and stably converge to the optimal solution, and improve the
optimization ability and efficiency of the algorithm. In order to verify the
effective ness of the method, five standard test functions are selected to
test the performance of HWQPSO, QPSO, DWC-QPSO and LTQPSO algorithm, and the
algorithms are applied to the task scheduling of the cloud computing
platform. Through the test experiment and application comparison, the results
show that the HWQPSO algorithm can converge to the optimal solution of the
test function faster than the other three algorithms. It can also find the
task scheduling scheme with the shortest time consumption and the most
balanced computing resource load in the cloud platform. In the experiment,
compared with QPSO, DWC-QPSO and LTQPSO algorithm, HWQPSO execution time of
the maximum task scheduling was reduced by 35%, 23% and 21% respectively.
The Design and Evaluation of a Strategy of Data Placement in Cloud Computing Platform
