The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
A New Multi-Swarms Competitive Particle Swarm Optimization Algorithm
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“PSO [4] is easy to implement and there are few parameters to adjust, but PSO is easy to get trapped in local optimum.It is the main reason for the premature convergence of PSO that the diversity of population gradually decays.”
From A Satellite Observation Data Transmission Scheduling Algorithm Oriented to Data Topics (2020) · cites this paper as [4]
In the citing paper
“PSO [4] is easy to implement and there are few parameters to adjust, but PSO is easy to get trapped in local optimum.”
From A Satellite Observation Data Transmission Scheduling Algorithm Oriented to Data Topics (2020) · cites this paper as [4]
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
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
Astrom, K. J. (2002) · Proceedings of 1994 IEEE International Symposium on Information Theory · doi:10.1109/isit.1994.394967 · Cited by 397
MCPSO: A multi-swarm cooperative particle swarm optimizer
Niu, B., Zhu, Y., He, X., & Wu, H. (2007) · Applied Mathematics and Computation · doi:10.1016/j.amc.2006.07.026 · Cited by 272
Multi-strategy ensemble particle swarm optimization for dynamic optimization
Du, W., & Li, B. (2008) · Information Sciences · doi:10.1016/j.ins.2008.01.020 · Cited by 253
Adaptive particle swarm optimization on individual level
Xie, X., Zhang, W.-J., & Yang, Z.-L. (2003) · doi:10.1109/icosp.2002.1180009 · Cited by 134
Hybrid particle swarm optimizer with mass extinction
Xie, X., Zhang, W.-J., & Yang, Z.-L. (2003) · International Conference on Communications, Circuits and Systems · doi:10.1109/icccas.2002.1178992 · Cited by 71
A multi-swarm optimizer based fuzzy modeling approach for dynamic systems processing
Niu, B., Zhu, Y., He, X., & Shen, H. Y. (2007) · Neurocomputing · doi:10.1016/j.neucom.2007.05.010 · Cited by 50
A Hierarchical Structure Poly-particle Swarm Optimization Algorithm
Tian, L. (2008) · Journal of Sichuan University · Cited by 3
Cited by
A Satellite Observation Data Transmission Scheduling Algorithm Oriented to Data Topics
Chen, H., Zhai, B., Wu, J., Du, C., & Li, J. (2020) · International Journal of Aerospace Engineering · doi:10.1155/2020/2180674 · Cited by 12
Metadata
- Authors
- Xia, L., Chu, J., & Geng, Z.
- Affiliations
- Beijing University of Chemical Technology (CN)
- Corresponding author
- Xia, Lirong
- Venue
- Lecture Notes in Electrical Engineering
- Publisher
- Springer Berlin Heidelberg
- Published
- 2012
- Pages
- 133-140
- ISSN
- 1876-1100 (ISSN-L)
- 1876-1119
- OpenAlex
- W67569981
- Cited by
- 1
- References
- 11
- Citations by year
- 2013: 1
- FWCI
- 0.57
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in electrical engineering (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Antenna Design and Optimization
- Keywords
- fuzzy c-means clustering, competitive learning, local optima avoidance, swarm diversity, global search ability
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: A New Multi-Swarms Competitive Particle Swarm Optimization Algorithm. https://app.cochat.ai/papers/10.1007/978-3-642-26001-8_18
- The paper
- Xia, L., Chu, J., & Geng, Z. (2012). A New Multi-Swarms Competitive Particle Swarm Optimization Algorithm. Lecture Notes in Electrical Engineering. https://doi.org/10.1007/978-3-642-26001-8_18
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.
