The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
A Novel Sigmoid-Function-Based Adaptive Weighted Particle Swarm Optimizer
Open accessAbstract
In this paper, a novel particle swarm optimization (PSO) algorithm is put forward where a sigmoid-function-based weighting strategy is developed to adaptively adjust the acceleration coefficients. The newly proposed adaptive weighting strategy takes into account both the distances from the particle to the global best position and from the particle to its personal best position, thereby having the distinguishing feature of enhancing the convergence rate. Inspired by the activation function of neural networks, the new strategy is employed to update the acceleration coefficients by using the sigmoid function. The search capability of the developed adaptive weighting PSO (AWPSO) algorithm is comprehensively evaluated via eight well-known benchmark functions including both the unimodal and multimodal cases. The experimental results demonstrate that the designed AWPSO algorithm substantially improves the convergence rate of the particle swarm optimizer and also outperforms some currently popular PSO algorithms.
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
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
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
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
Empirical study of particle swarm optimization
Shi, Y., & Eberhart, R. C. (2003) · Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406) · doi:10.1109/cec.1999.785511 · Cited by 3,945
Evolutionary programming made faster
Yao, X., Liu, Y., & Lin, G. (1999) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.771163 · Cited by 3,910
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2005.857610 · Cited by 3,743
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
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
Comparing 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
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
del Valle, Y., Venayagamoorthy, G. K., Mohagheghi, S., Hernandez, J.-C., & Harley, R. G. (2008) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2007.896686 · Cited by 2,277
Cited by
A review on the attention mechanism of deep learning
Niu, Z., Zhong, G., & Yu, H. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.091 · Cited by 3,379
Particle Swarm Optimization: A Comprehensive Survey
Shami, T. M., El-Saleh, A. A., Alswaitti, M., Al-Tashi, Q., Summakieh, M. A., & Mirjalili, S. (2022) · IEEE Access · doi:10.1109/access.2022.3142859 · Cited by 1,584
Song, B., Wang, Z., & Zou, L. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106960 · Cited by 441
Self-citationDeep multi-view learning methods: A review
Yan, X., Hu, S., Mao, Y., Ye, Y., & Yu, H. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.090 · Cited by 322
A Dynamic Neighborhood-Based Switching Particle Swarm Optimization Algorithm
Zeng, N., Wang, Z., Liu, W., Zhang, H., Hone, K., & Liu, X. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.3029748 · Cited by 269
Self-citation
Metadata
- Authors
- Liu, W., Wang, Z., Yuan, Y., Zeng, N., Hone, K., & Liu, X.
- Venue
- IEEE Transactions on Cybernetics
- Published
- 2021-02
- Volume
- 51
- Issue
- 2
- Pages
- 1085-1093
- ISSN
- 2168-2267 (ISSN-L)
- 2168-2275
- OpenAlex
- W2963358003
- Cited by
- 346
- References
- 26
- FWCI
- 13.12
- Type
- journal article
- Language
- en
- Open access
- green
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 Novel Sigmoid-Function-Based Adaptive Weighted Particle Swarm Optimizer. https://app.cochat.ai/papers/10.1109/tcyb.2019.2925015
- The paper
- Liu, W., Wang, Z., Yuan, Y., Zeng, N., Hone, K., & Liu, X. (2021). A Novel Sigmoid-Function-Based Adaptive Weighted Particle Swarm Optimizer. IEEE Transactions on Cybernetics. https://doi.org/10.1109/tcyb.2019.2925015
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.
