Simplifying particle swarm optimization

Pedersen, M.E.H and Chipperfield, A.J. (2010) Simplifying particle swarm optimization. Applied Soft Computing, 10, (2) (doi:10.1016/j.asoc.2009.08.029).


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The general purpose optimization method known as Particle Swarm Optimization (PSO) has received much attention in past years, with many attempts to find the variant that performs best on a wide variety of optimization problems. The focus of past research has been with making the PSO method more complex as this is frequently believed to increase its adaptability to other optimization problems. This study takes the opposite approach and simplifies the PSO method. To compare the efficacy of the original PSO and the simplified variant here, an easy technique is presented for efficiently tuning their behavioural parameters. The technique works by employing an overlaid meta-optimizer, which is capable of simultaneously tuning parameters with regard to multiple optimization problems, wheras previous approaches to meta-optimization have buned behavioural parameters to work well on just a single optimization problem. It is then found that not only the PSO method and its simplified variant have comparable performance for optimization a number of Artificial Neural Network problems, but also the simplified variant appears to offer a small improvement in some cases

Item Type: Article
Digital Object Identifier (DOI): doi:10.1016/j.asoc.2009.08.029
ISSNs: 1568-4946 (print)
Related URLs:
Keywords: numerical optimization, stochastic, swarm, turning, simplifying
Subjects: Q Science > Q Science (General)
Divisions : University Structure - Pre August 2011 > School of Engineering Sciences > Computational Engineering and Design
ePrint ID: 71755
Accepted Date and Publication Date:
March 2010Published
7 April 2008Submitted
Date Deposited: 23 Dec 2009
Last Modified: 31 Mar 2016 13:03

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