Application of an Improved Immune Evolutionary Algorithm to Grid Task Scheduling

Sheng Jun Xue; Fang Fang Liu

doi:10.4028/www.scientific.net/AMR.219-220.1383

Paper Titles

SPWM Inverter Closed-Loop PID Control System
p.1367

Damping Controller Based on Output Prediction Observer and LMI Method
p.1371

Optimization of Phase Change Materials Used in Asphalt Pavement to Prevent Rutting
p.1375

A New Mechanical Vibration Source Number Estimation Method Based on EMD-SVD-DE
p.1379

Application of an Improved Immune Evolutionary Algorithm to Grid Task Scheduling
p.1383

Design and Model Analysis of City Bus Body Frame Based on ANSYS
p.1389

The Short-Term Load Forecasting Model Based on Improved Gene Expression Programming Arithmetic
p.1395

Machine Frame Optimization of Pneumatic-Hydraulic Supercharging Press of Microparts
p.1399

Analysis on the Acquisition between Ping an Insurance Company of China, Ltd and Shenzhen Development Bank Limited Company
p.1403

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 219-220Application of an Improved Immune Evolutionary...

Application of an Improved Immune Evolutionary Algorithm to Grid Task Scheduling

Abstract:

Grid task scheduling is an NP problem , performance of scheduling algorithms greatly influences scheduling results. Aiming at the shortages of the existing Evolutionary Algorithm, such as premature convergence, search process easily falling into local optimum, poor scheduling results and so on, this paper proposed an improved immune Evolutionary Algorithm which introduced concentration mechanism in the immune system into Immune Evolutionary Algorithm and adjusted regulator to adaptive function. Simulation experiment shows that, convergence speed and performance of the improved algorithm are significantly improved and it can better converge to global optimal solution, applying the algorithm to grid task scheduling can obtain better scheduling results.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 219-220)

Pages:

1383-1388

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.219-220.1383

Citation:

Cite this paper

Online since:

March 2011

Authors:

Sheng Jun Xue, Fang Fang Liu

Keywords:

Affinity, Immune Evolutionary Algorithm, Regulator, Task Scheduling

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Shengjun Xue,Tinghuai Ma, Wenjie Liu.Globus Toolkit 4: Java gird service programming [M].Beijing: Tsinghua University Press(2009).

Google Scholar

[2] Braun T,SiegelH,BeckN.A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. Journal of Parallel and Distributed Computing Vol.61 Issue.6(2001),pp.810-837.

DOI: 10.1006/jpdc.2000.1714

Google Scholar

[3] Xunxue Cui. Multi-objective evolutionary algorithm and its application [M].Beijing: National Defense Industry Press(2008),pp.94-99.

Google Scholar

[4] Deming Lei, Xinping Yan.Multi-objective Optimization Algorithm and its application [M].Beijing: Science Press(2009),pp.23-30.

Google Scholar

[5] Minyou Chen,Yucong zhang,Ciyong Luo.Adaptive evolutionary multi-objective particle swarm optimization algorithm [J].Control and Decision Vol.24 Issue.12(2009).

Google Scholar

[6] Yubao Cui, Jianyi Li, Guixiang Xue.An improved heuristic grid task scheduling algorithm [J].Journal of Tianji University of Technology and Education Issue.16(2006).

Google Scholar

[7] Y Ishida, N Adachi.Immune algorithm for multi-agent: application to adaptive noise neutralization In: IEEE International Conference on Intelligence Robots and System(1996).

Google Scholar

[8] Yangfan Zhao.Grid task scheduling strategies based on Genetic Algorithm and Ant Colony Optimization [M].Ocean University of China(2006).

Google Scholar