The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Particle Swarm Optimization
Abstract
this paper is to show how the search algorithm known as particle swarm optimization performs. Here, particle swarm optimization is applied to structural design problems, but the method has a much wider range of possible applications. The paper's new contributions are improvements to the particle swarm optimization algorithm and conclusions and recommendations as to the utility of the algorithm. Results of numerical experiments for both continuous and discrete applications are presented in the paper. The results indicate that the particle swarm optimization algorithm does locate the constrained minimum design in continuous applications with very good precision, albeit at a much higher computational cost than that of a typical gradient based optimizer. However, the true potential of particle swarm optimization is primarily in applications with discrete and/or discontinuous functions and variables. Additionally, particle swarm optimization has the potential of e#cient computation with very large numbers of concurrently operating processors
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Citations
Cites
Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192
A new optimizer using particle swarm theory
Eberhart, R., & Kennedy, J. (2002) · MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science · doi:10.1109/mhs.1995.494215 · Cited by 16,067
A modified particle swarm optimizer
Shi, Y., & Eberhart, R. (2002) · 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360) · doi:10.1109/icec.1998.699146 · Cited by 10,229
Integer and Combinatorial Optimization
Williams, H. P., Nemhauser, G. L., & Wolsey, L. A. (1990) · Journal of the Operational Research Society · doi:10.2307/2583737 · Cited by 5,485
Parameter selection in particle swarm optimization
Shi, Y., & Eberhart, R. C. (1998) · Lecture Notes in Computer Science · doi:10.1007/bfb0040810 · Cited by 3,538
Integer and Combinatorial Optimization
Nemhauser, G., & Wolsey, L. (1988) · Wiley eBooks · doi:10.1002/9781118627372 · Cited by 3,176
The particle swarm optimization algorithm: convergence analysis and parameter selection
Trelea, I. C. (2003) · Information Processing Letters · doi:10.1016/s0020-0190(02)00447-7 · Cited by 2,644
The swarm and the queen: towards a deterministic and adaptive particle swarm optimization
Clerc, M. (2003) · Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406) · doi:10.1109/cec.1999.785513 · Cited by 1,692
Numerical optimization techniques for engineering design
Vanderplaats, G. N. (1999) · Medical Entomology and Zoology · Cited by 1,204
Evolutionary Algorithms in Engineering Applications
(1997) · Springer eBooks · doi:10.1007/978-3-662-03423-1 · Cited by 705
Cited by
Das, S., Abraham, A., & Konar, A. (2008) · Studies in Computational Intelligence · doi:10.1007/978-3-540-78297-1_1 · Cited by 429
Larsen, S., Majone, B., Zulian, P., Stella, E., Bellin, A., Bruno, M. C., & Zolezzi, G. (2021) · Water Resources Research · doi:10.1029/2020wr028496 · Cited by 28
“10.1029/2020WR028496 4 of 17 Water Resources Research according to the Nash-Sutcliffe efficiency index (NSE; Nash & Sutcliffe, 1970), by using the Particle Swarming Optimization algorithm (Kennedy & Eberhart, 1995).”
MethodInfluential
Metadata
- Authors
- Venter, G., & Sobieszczanski-Sobieski, J.
- Venue
- AIAA Journal
- Published
- 2003-08
- Volume
- 41
- Issue
- 8
- Pages
- 1583-1589
- ISSN
- 0001-1452 (ISSN-L)
- 1533-385X
- OpenAlex
- W2116850883
- Cited by
- 802
- References
- 12
- FWCI
- 17.31
- Type
- journal article
- Language
- en
- Open access
- closed
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