Particle Swarm Optimization Technique for Task-Resource Scheduling for Robotic Clouds

Vladimir Popov

doi:10.4028/www.scientific.net/AMM.565.243

Paper Titles

Emulator of Communication of SATEX-II Satellite with Earth Station
p.223

Errors of Timing Diagram of Automatic Machine
p.228

Complication Evaluation of Methods of Naval Battlefield Electromagnetic Environment Based on Combat Effectiveness
p.233

Research of a New Method to Predict Ocean Wave Spectra
p.238

Particle Swarm Optimization Technique for Task-Resource Scheduling for Robotic Clouds
p.243

Study on the Grasping Ability and Optimization of Multi-Fingered Robot Hands under Different Postures
p.247

Construction of Point Set Surfaces through Quadric Polynomials
p.253

Research on Safe Shipment of the Cargoes which May Liquefy
p.260

The Research on Methods of Complicated Electromagnetic Environment Construction
p.265

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 565Particle Swarm Optimization Technique for...

Particle Swarm Optimization Technique for Task-Resource Scheduling for Robotic Clouds

Abstract:

The task-resource scheduling problem is one of the fundamental problems for cloud computing. There are a large number of heuristics based approaches to various scheduling workflow applications. In this paper, we consider the problem for robotic clouds. We propose new method of selection of parameters of a particle swarm optimization algorithm for solution of the task-resource scheduling problem for robotic clouds. In particular, for the prediction of values of the inertia weight we consider genetic algorithms, multilayer perceptron networks with gradient learning algorithm, recurrent neural networks with gradient learning algorithm, and 4-order Runge Kutta neural networks with different learning algorithms. Also, we present experimental results for different intelligent algorithms.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 565)

Pages:

243-246

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.565.243

Citation:

Cite this paper

Online since:

June 2014

Authors:

Vladimir Popov

Keywords:

Cloud Computing, Particle Swarm Optimization (PSO), Robotic Cloud, Runge Kutta Neural Network, Task-Resource Scheduling Problem

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] J. Arshad, P. Townend, J. Xu: International Journal of Automation and Computing Vol. 8 (2011), p.286.

Google Scholar

[2] A. Gorbenko, V. Popov: International Journal of Automation and Computing Vol. 9 (2012), p.429.

Google Scholar

[3] B. Hu, X. Zhang, X. Zhang: JICS Vol. 10 (2013), p.5945.

Google Scholar

[4] J.D. Ullman: J. Comput. System Sci. Vol. 10 (1975) p.384.

Google Scholar

[5] J. Yu, R. Buyya, K. Ramamohanarao: Studies in Computational Intelligence Vol. 146 (2008), p.173.

Google Scholar

[6] S. Pandey, L. Wu, S. M. Guru, R. Buyya, in: Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications, IEEE press (2010).

Google Scholar

[7] L. Zhang, Y. Chen, R. Sun, S. Jing, B. Yang: IJCIR Vol. 4 (2008), p.37.

Google Scholar

[8] A. Jordehi, J. Jasni: J. Exp. Theor. Artif. In. Vol. 25 (2013), p.527.

Google Scholar

[9] A. Gorbenko, V. Popov: Advanced Materials Research Vol. 683 (2013), p.909.

Google Scholar

[10] V. Popov: AIP Conf. Proc. Vol. 1558 (2013), p. (2083).

Google Scholar

[11] V. Popov: Applied Mechanics and Materials Vol. 365-366 (2013), p.946.

Google Scholar

[12] L. Guo, S. Zhao, S. Shen, C. Jiang: J. Network. Vol. 7 (2012), p.547.

Google Scholar

[13] Y. -J. Wang, C. -T. Lin: IEEE T. Neural Networ. Vol. 9 (1998), p.294.

Google Scholar