The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Stereotyping: improving particle swarm performance with cluster analysis
Abstract
Individuals in the particle swarm population were "stereotyped" by cluster analysis of their previous best positions. The cluster centers then were substituted for the individuals' and neighbors' best previous positions in the algorithm. The experiments, which were inspired by the social-psychological metaphor of social stereotyping, found that performance could be generally improved by substituting individuals', but not neighbors', cluster centers for their previous bests.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The particle swarm - explosion, stability, and convergence in a multidimensional complex space
Clerc, M., & Kennedy, J. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.985692 · Cited by 9,017
Self-citationComparing inertia weights and constriction factors in particle swarm optimization
Eberhart, R. C., & Shi, Y. (2002) · Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512) · doi:10.1109/cec.2000.870279 · Cited by 2,966
On the relationship between autobiographical memory and perceptual learning.
Jacoby, L. L., & Dallas, M. (1981) · Journal of Experimental Psychology: General · doi:10.1037//0096-3445.110.3.306 · Cited by 2,553
Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance
Kennedy, J. (2003) · Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406) · doi:10.1109/cec.1999.785509 · Cited by 1,061
Self-citationUsing selection to improve particle swarm optimization
Angeline, P. J. (2002) · 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360) · doi:10.1109/icec.1998.699327 · Cited by 864
Self-categorization theory and social influence.
Turner, J. C., & Oakes, P. J. (1989) · Medical Entomology and Zoology · Cited by 272
Reference Groups: Exploration into Conformity and Deviation of Adolescents
Giddens, A., Sherif, M., & Sherif, C. W. (1964) · The British Journal of Sociology · doi:10.2307/588879 · Cited by 167
Reference Groups: Exploration into Conformity and Deviation of Adolescents
SILVERMAN, I. W., Sherif, M., & Sherif, C. W. (1966) · The American Journal of Psychology · doi:10.2307/1420740 · Cited by 32
Cited by
Particle swarm optimization: developments, applications and resources
Eberhart, & Shi, Y. (2001) · Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546) · doi:10.1109/cec.2001.934374 · Cited by 4,310
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
Self-Organizing Hierarchical Particle Swarm Optimizer With Time-Varying Acceleration Coefficients
Ratnaweera, A., Halgamuge, S. K., & Watson, H. C. (2004) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2004.826071 · Cited by 3,034
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
Particle swarm optimization in electromagnetics
Robinson, J., & Rahmat-Samii, Y. (2004) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2004.823969 · Cited by 2,265
Metadata
- Authors
- Kennedy, J.
- Venue
- Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)
- Publisher
- IEEE
- Published
- 2002-11-07
- Volume
- 2
- Pages
- 1507-1512
- OpenAlex
- W1855408646
- Cited by
- 354
- References
- 8
- FWCI
- 10.60
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Stereotyping: improving particle swarm performance with cluster analysis. https://app.cochat.ai/papers/10.1109/cec.2000.870832
- The paper
- Kennedy, J. (2002). Stereotyping: improving particle swarm performance with cluster analysis. Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512). https://doi.org/10.1109/cec.2000.870832
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
