An Improved Particle Swarm Optimization Algorithm with Invasive Weed

Huan Zhao; Jiang Long Yu; Arash Tahmasebi; Pei Hong Wang

doi:10.4028/www.scientific.net/AMR.621.356

Paper Titles

Line Loss Rate Forecasting Based on Grey Model and Combination of Neural Network
p.340

The Classification Research about the Uncertain Factors of Job Shop Manufacturing Shop Re-Scheduling
p.344

An Embedded Positioning System Applied to the Seismic Exploration
p.348

A Simulation Model for Multi-Factor Sensitivity Analysis on Energy Demand
p.352

An Improved Particle Swarm Optimization Algorithm with Invasive Weed
p.356

Monitoring of Efficiency and NOx Emissions at a Coal-Fired Utility Boiler
p.360

Color Image Segmentation Based on Dynamic Model and Power Spectrum
p.364

Study on Integrated Project Delivery Construction Project Collaborative Application Based on Building Information Model
p.370

Study on Electronic Kanban Management System in Steel Structure Engineering of an International Expo Centre
p.375

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 621An Improved Particle Swarm Optimization Algorithm...

An Improved Particle Swarm Optimization Algorithm with Invasive Weed

Abstract:

This paper presents a hybrid algorithm based on invasive weed optimization (IWO) and particle swarm optimization (PSO), named IW-PSO. IWO is a relatively novel numerical stochastic optimization algorithm. By incorporating the reproduction and spatial dispersal of IWO into the traditional PSO, exploration and exploitation of the PSO can be enhanced and well balanced to achieve better performance. In a set of 15 test function problem, the parameters of IW-PSO were analyzed and selected, and the computational results show that IW-PSO can effectively obtain higher quality solutions so as to avoid being trapped in local optimum, comparing with PSO and IWO.

You might also be interested in these eBooks

Advances on Material Science and Manufacturing Technologies

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 621)

Pages:

356-359

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.621.356

Citation:

Cite this paper

Online since:

December 2012

Authors:

Huan Zhao, Jiang Long Yu, Arash Tahmasebi, Pei Hong Wang

Keywords:

Hybrid Algorithm, Invasive Weed Optimization, Particle Swarm Optimization (PSO)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Kennedy, J., Eberhart, R.C., Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks. 1995. p.1942-(1948).

Google Scholar

[2] He, S., Wu, Q.H., Wen, Y.J., Saunders, J.R., Paton, R.C., A particle swarm optimizer with passive congregation. BioSystems 2004. 78, 135-147.

DOI: 10.1016/j.biosystems.2004.08.003

Google Scholar

[3] Angeline, P., Evolutionary optimization versus particle swarm optimization: philosophy and performance difference. In: Proceedings of the Evolutionary Programming Conference, San Diego, USA. (1998).

DOI: 10.1007/bfb0040811

Google Scholar

[4] Zhao, B., Guo, C.X., Bai, B.R., Cao, Y.J., An improved particle swarm optimization algorithm for unit commitment. Electrical Power & Energy systems 2006. 28, 482-490.

DOI: 10.1016/j.ijepes.2006.02.011

Google Scholar

[5] Li, L.L., Wang, L., Liu, L.H., An effective hybrid PSOSA strategy for optimization and its application to parameter estimation. Applied Mathematics and Computation 2006. 179, 135-146.

DOI: 10.1016/j.amc.2005.11.086

Google Scholar

[6] Mehrabian, A.R., Lucas, C., A novel numerical optimization algorithm inspired from weed colonization. Ecological Informatics 2006. 1(4), 355-366.

DOI: 10.1016/j.ecoinf.2006.07.003

Google Scholar