The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Bare bones particle swarms
Abstract
The particle swarm algorithm has just enough moving parts to make it hard to understand. The formula is very simple, it is even easy to describe the working of the algorithm verbally, yet it is very difficult to grasp in one's mind how the particles oscillate around centers that are constantly changing; how they influence one another; how the various parameters affect the trajectory of the particle; how the topology of the swarm affects its performance; and so on. This paper strips away some traditional features of the particle swarm in the search for the properties that make it work. The particle swarm algorithm is modified by eliminating the velocity formula. Variations are compared. In the process some of the mysteries of the algorithm are revealed, we discover its similarity to other stochastic population-based problem solving methods, and new avenues of investigation are suggested or implied.
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Cites
Blind variation and selective retentions in creative thought as in other knowledge processes
Campbell, D. T. (1960) · Psychological Review · doi:10.1037/h0040373 · Cited by 2,329
Cited by
Differential Evolution: A Survey of the State-of-the-Art
Das, S., & Suganthan, P. N. (2011) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2010.2059031 · Cited by 5,307
Particle swarm optimization algorithm: An overview
Wang, D., Tan, D., & Liu, L. (2018) · Soft Computing · doi:10.1007/s00500-016-2474-6 · Cited by 3,343
A social learning particle swarm optimization algorithm for scalable optimization
Cheng, R., & Jin, Y. (2015) · Information Sciences · doi:10.1016/j.ins.2014.08.039 · Cited by 747
Cooperatively Coevolving Particle Swarms for Large Scale Optimization
Li, X., & Yao, X. (2012) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2011.2112662 · Cited by 745
Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application
Houssein, E. H., Gad, A. G., Hussain, K., & Suganthan, P. N. (2021) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2021.100868 · Cited by 466
Retracted
Metadata
- Authors
- Kennedy, J.
- Venue
- Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706)
- Publisher
- IEEE
- Published
- 2004-03-22
- Pages
- 80-87
- OpenAlex
- W2127931254
- Cited by
- 690
- References
- 1
- FWCI
- 12.58
- Type
- conference paper
- Language
- en
- Open access
- closed
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