Using quantum-behaved particle swarm optimization algorithm to solve non-linear programming problems

Source: International Journal of Computer Mathematics, Volume 84, Number 2, February 2007 , pp. 261-272(12)

Publisher: Taylor and Francis Ltd

Abstract:

In this paper, we focus on solving non-linear programming (NLP) problems using quantum-behaved particle swarm optimization (QPSO). After a brief introduction to the original particle swarm optimization (PSO), we describe the origin and development of QPSO, and the penalty function method for constrained NLP problems. The performance of QPSO is tested on some unconstrained and constrained benchmark functions and compared with PSO with inertia weight (PSO-In) and PSO with constriction factor (PSO-Co). The experimental results show that QPSO outperforms the traditional PSOs and is a promising optimization algorithm.

Keywords: Non-linear programming; Heuristics; Evolutionary computation; PSO; Quantum behaviour

Document Type: Research article

DOI: http://dx.doi.org/10.1080/00207160601170254

Publication date: 2007-02-01

More about this publication?

• Information for Authors
• Subscribe to this Title
• ingentaconnect is not responsible for the content or availability of external websites

Related content

• In this: publication
• By this: publisher
• In this Subject: Computer Science ,  Mathematics and Statistics

Website © Publishing Technology. Article copyright remains with the publisher, society or author(s) as specified within the article.

• Terms and conditions
• Privacy policy
• Information for advertisers