Earticle

초록

영어

The paper proposed a network scheduling in cloud computing based on intelligence Particle Swarm Optimization algorithm aimed at the disadvantages of cloud computing network scheduling. Firstly, on the basis of cloud model, used intelligence Particle Swarm Optimization algorithm with strong ability of global searching to find the better solution of cloud computing network scheduling then turned the better solution into the initial pheromone of improved Particle Swarm Optimization algorithm, and found out the cloud computing network scheduling and the algorithm’s global optimal solution through improved Particle Swarm Optimization information communications and feedbacks. Finally, made comparison test of the three benchmark function on the basis of MATLAB, the results showed, compared with traditional intelligence Particle Swarm Optimization algorithms, the improved algorithm can preferably allocate the resources in cloud computing model, the effect of prediction model time is more close to actual time, can efficiently limit the possibility of falling into local convergence, the optimal solution’s time of objective function value is shorten which meet the user’s needs more.

목차

Abstract
 1. Introduction
 2. Description Process
 3. Camera parameters calibration based on Quantum Particle Swarm Optimization.
  3.1. The Evolution Function
  3.2. The Fitness Function
  3.3. Algorithm Implementation
 4. Experimental Results
  4.1. Performance Test of Algorithm
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Ke Lu [ Department of international education, Jiaozuo University, Jiaozuo, 454003, China ]
• Junxia Meng [ College of Information Engineering, Jiaozuo University, Jiaozuo, 454003, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605