A Strongly Interacting Dynamic Particle Swarm  Optimization Method

Kok, S.; Snyman, J. A.

doi:https://doi.org/10.1155/2008/126970

Journal of Artificial Evolution and Applications

On this page

Abstract References Copyright Related Articles

Special Issue

Particle Swarms: The Second Decade

View this Special Issue

Research Article | Open Access

Volume 2008 | Article ID 126970 | https://doi.org/10.1155/2008/126970

A Strongly Interacting Dynamic Particle Swarm Optimization Method

S. Kok¹and J. A. Snyman¹

Academic Editor: Riccardo Poli

Received24 Jul 2007

Accepted18 Dec 2007

Published31 Mar 2008

Abstract

A novel dynamic interacting particle swarm optimization algorithm (DYN-PSO) is proposed. The algorithm can be considered to be the synthesis of two established trajectory methods for unconstrained minimization. In the new method, the minimization of a function is achieved through the dynamic motion of a strongly interacting particle swarm, where each particle in the swarm is simultaneously attracted by all other particles located at positions of lower function value. The force of attraction experienced by a particle at higher function value due to a particle at a lower function value is equal to the difference between the respective function-values divided by their stochastically perturbed position difference. The resultant motion of the particles under the influence of the attracting forces is computed by solving the associated equations of motion numerically. An energy dissipation strategy is applied to each particle. The specific chosen force law and the dissipation strategy result in the rapid collapse (convergence) of the swarm to a stationary point. Numerical results show that, in comparison to the standard particle swarm algorithm, the proposed DYN-PSO algorithm is promising.

References

K. W. Kolodziej and J. Hjelm, Local Positioning Systems: LBS Applications and Services, CRC Press, Boca Raton, Fla, USA, 2006.
A. LaMarca, Y. Chawathe, S. Consolvo et al., “Place lab: device positioning using radio beacons in the wild,” in Proceedings of the 3rd International Conference on Pervasive Computing (PERVASIVE '05), vol. 3468, pp. 116–133, Munich, Germany, May 2005.
View at: Publisher Site | Google Scholar
J. Letchner, D. Fox, and A. LaMarca, “Large-scale localization from wireless signal strength,” in Proceedings of the 20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference (AAAI '05), pp. 15–20, Pittsburgh, Pa, USA, July 2005.
View at: Google Scholar
H. Lim, L.-C. Kung, J. C. Hou, and H. Luo, “Zero-configuration, robust indoor localization: theory and experimentation,” in Proceedings of the 25th IEEE International Conference on Computer Communications (INFOCOM '06), pp. 1–12, Barcelona, Spain, April 2006.
View at: Publisher Site | Google Scholar
P. Bahl and V. N. Padmanabhan, “RADAR: an in-building RF-based user location and tracking system,” in Proceedings of the 19th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM '00), vol. 2, pp. 775–784, Tel Aviv, Israel, March 2000.
View at: Publisher Site | Google Scholar
K. Kaemarungsi and P. Krishnamurthy, “Properties of indoor received signal strength for WLAN location fingerprinting,” in Proceedings of the 1st Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services (MOBIQUITOUS '04), pp. 14–23, Boston, Mass, USA, August 2004.
View at: Publisher Site | Google Scholar
D. Niculescu and B. Nath, “Ad hoc positioning system (APS),” in Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM '01), vol. 5, pp. 2926–2931, San Antonio, Tex, USA, November 2001.
View at: Publisher Site | Google Scholar
D. Niculescu and B. Nath, “Ad hoc positioning system (APS) using AOA,” in Proceedings of the 22nd Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM '03), vol. 3, pp. 1734–1743, San Francisco, Calif, USA, March-April 2003.
View at: Google Scholar
R. Peng and M. L. Sichitiu, “Angle of arrival localization for wireless sensor networks,” in Proceedings of the 3rd Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON '06), vol. 1, pp. 374–382, Reston, Va, USA, September 2006.
View at: Publisher Site | Google Scholar
R. L. Moses, D. Krishnamurthy, and R. M. Patterson, “A self-localization method for wireless sensor networks,” EURASIP Journal on Applied Signal Processing, vol. 2003, no. 4, pp. 348–358, 2003.
View at: Publisher Site | Google Scholar
D. Niculescu and B. Nath, “Position and orientation in ad hoc networks,” Ad Hoc Networks, vol. 2, no. 2, pp. 133–151, 2004.
View at: Publisher Site | Google Scholar
N. Bulusu, J. Heidemann, and D. Estrin, “GPS-less low-cost outdoor localization for very small devices,” IEEE Personal Communications, vol. 7, no. 5, pp. 28–34, 2000.
View at: Publisher Site | Google Scholar
C. Savarese, J. M. Rabaey, and K. Langendoen, “Robust positioning algorithms for distributed Ad-Hoc wireless sensor networks,” in Proceedings of USENIX Annual Technical Conference, pp. 317–327, Monterey, Calif, USA, June 2002.
View at: Google Scholar

Copyright

Copyright © 2008 S. Kok and J. A. Snyman. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

388

Downloads

0

Citations