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

Weitian Lin | Zendy; Zhigang Lian | Zendy; Xingsheng Gu | Zendy; Bin Jiao | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

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

Author(s) -

Weitian Lin,

Zhigang Lian,

Xingsheng Gu,

Bin Jiao

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/905712

Subject(s) - benchmark (surveying) , particle swarm optimization , convergence (economics) , mathematical optimization , multi swarm optimization , local search (optimization) , set (abstract data type) , algorithm , metaheuristic , computer science , swarm behaviour , local optimum , mathematics , geodesy , geography , economics , programming language , economic growth

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

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research