Research on Improved Hybrid Particle Swarm Optimization Algorithm for Cloud Computing Task Scheduling

DOI

10.2991/meici-18.2018.234How to use a DOI?

Keywords

Cloud computing; Task scheduling; Ant colony algorithm; Hybrid PSO algorithm

Abstract

In the cloud computing environment, one of the hot spot of researches in cloud computing is how to accomplish the service request in numerous running tasks. This paper puts forward an improved hybrid particle swarm optimization, firstly using particle swarm algorithm as the main level algorithm, the initial solution rapidly, followed by the max min ant colony algorithm, as the algorithm to find the optimal solution based on the initial solution. Finally, the availability and advantage of the proposed algorithm can be tested through the simulation experiment.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Xiaoguang Yang
AU  - Qian Wang
AU  - Yimin Zhang
PY  - 2018/12
DA  - 2018/12
TI  - Research on Improved Hybrid Particle Swarm Optimization Algorithm for Cloud Computing Task Scheduling
BT  - Proceedings of the 2018 8th International Conference on Management, Education and Information (MEICI 2018)
PB  - Atlantis Press
SP  - 1162
EP  - 1167
SN  - 1951-6851
UR  - https://doi.org/10.2991/meici-18.2018.234
DO  - 10.2991/meici-18.2018.234
ID  - Yang2018/12
ER  -