Shape and size optimization problems instructural design are addressed using the particle swarm optimization algorithm (PSOA). In our implementation of the PSOA, the social behaviour of birds is mimicked. Individual birds exchange information about their position, velocity and fitness, and the behaviour of the flock is then influenced to increase the probability of migration to regions of high fitness. New operators in the PSOA, namely the elite velocity and the elite particle, are introduced.
Standard size and shape design problems selected from literature are used to evaluate the performance of the PSOA. The performance of the PSOA is compared with that of three gradient based methods, as well as the genetic algorithm (GA). In attaining the approximate region of the optimum, our implementation suggests that the PSOA is superior to the GA, and comparable to gradient based algorithms.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price excludes VAT (USA)
Tax calculation will be finalised during checkout.
Received December 18, 2000
About this article
Cite this article
Fourie, P., Groenwold, A. The particle swarm optimization algorithm in size and shape optimization. Struct Multidisc Optim 23, 259–267 (2002). https://doi.org/10.1007/s00158-002-0188-0