A Local and Global Search Combined Particle Swarm Optimization Algorithm and Its Convergence Analysis

Weitian Lin, Zhigang Lian, Xingsheng Gu and Bin Jiao

Mathematical Problems in Engineering, 2014, vol. 2014, 1-11

Abstract:

Particle swarm optimization algorithm (PSOA) is an advantage optimization tool. However, it has a tendency to get stuck in a near optimal solution especially for middle and large size problems and it is difficult to improve solution accuracy by fine-tuning parameters. According to the insufficiency, this paper researches the local and global search combine particle swarm algorithm (LGSCPSOA), and its convergence and obtains its convergence qualification. At the same time, it is tested with a set of 8 benchmark continuous functions and compared their optimization results with original particle swarm algorithm (OPSOA). Experimental results indicate that the LGSCPSOA improves the search performance especially on the middle and large size benchmark functions significantly.

Date: 2014
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2014/905712.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2014/905712.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:905712

DOI: 10.1155/2014/905712

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().