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Mathematical Problems in Engineering
Volume 2014, Article ID 905712, 11 pages
Research Article

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

1Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, Shanghai 200237, China
2Shanghai Dianji University, Shanghai 201306, China

Received 4 December 2013; Accepted 6 February 2014; Published 16 March 2014

Academic Editor: Huaicheng Yan

Copyright © 2014 Weitian Lin et al. 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.


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.