A Local and Global Search Combined Particle Swarm Optimization Algorithm and Its Convergence Analysis. (16th March 2014)

• Record Type: Journal Article
• Authors: Lin, Weitian; Lian, Zhigang; Gu, Xingsheng; Jiao, Bin
• Other Names: Yan Huaicheng Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2014(2014)
• Publication Date: 2014-03-16
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2014/905712
• Languages: English
• ISSNs: 1024-123X

Abstract

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.