The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Enhancing differential evolution algorithm with a fitness-distance-based selection strategy
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
“Huang et al.29 introduced a fitness feedback mechanism into the DE algorithm24, though the stability of their method under complex constraints remains to be verified.”
From The optimization of low Earth orbit satellite constellation visibility with genetic algorithm for improved navigation potential (2025) · cites this paper as Huang et al.29
In the citing paper
“Kleinboehl et al.34 further demonstrated a strong correlation between the spacing of adjacent orbital planes () and the accuracy of atmospheric parameter inversion, using vertical profiles of greenhouse gases retrieved from a polar-orbiting satellite constellation29.”
From The optimization of low Earth orbit satellite constellation visibility with genetic algorithm for improved navigation potential (2025) · cites this paper as [29]
Citations
Cites
Storn, R., & Price, K. (1997) · Journal of Global Optimization · doi:10.1023/a:1008202821328 · Cited by 29,569
The Whale Optimization Algorithm
Mirjalili, S., & Lewis, A. (2016) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2016.01.008 · Cited by 14,581
JADE: Adaptive Differential Evolution With Optional External Archive
Zhang, J., & Sanderson, A. C. (2009) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2009.2014613 · Cited by 3,739
Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization
Qin, A. K., Huang, V. L., & Suganthan, P. N. (2009) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2008.927706 · Cited by 3,614
Improving the search performance of SHADE using linear population size reduction
Tanabe, R., & Fukunaga, A. S. (2014) · 2014 IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2014.6900380 · Cited by 1,625
Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters
Wang, Y., Cai, Z., & Zhang, Q. (2011) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2010.2087271 · Cited by 1,432
Success-history based parameter adaptation for Differential Evolution
Tanabe, R., & Fukunaga, A. (2013) · 2013 IEEE Congress on Evolutionary Computation · doi:10.1109/cec.2013.6557555 · Cited by 1,367
A comparative study of differential evolution variants for global optimization
Mezura-Montes, E., Velázquez-Reyes, J., & Coello Coello, C. A. (2006) · Proceedings of the 8th annual conference on Genetic and evolutionary computation · doi:10.1145/1143997.1144086 · Cited by 593
Single objective real-parameter optimization: Algorithm jSO
Brest, J., Maucec, M. S., & Boskovic, B. (2017) · 2017 IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2017.7969456 · Cited by 446
Improved Multi-operator Differential Evolution Algorithm for Solving Unconstrained Problems
Sallam, K. M., Elsayed, S. M., Chakrabortty, R. K., & Ryan, M. J. (2020) · 2020 IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec48606.2020.9185577 · Cited by 242
Research works printed but not linked: 9
- Hameed A, Aboobaider B, Mutar M et al (2020) A new hybrid approach based on discrete differential evolution algorithm to enhancement solutions of quadratic assignment problem. Int J Ind Eng Comput 11(1):51–72
- Zhao F, Zhao L, Wang L et al (2020) An ensemble discrete differential evolution for the distributed blocking flowshop scheduling with minimizing makespan criterion. Expert Syst Appl 160:113678
- Wang R, Fan F, Shen F et al (2021) Application of differential evolution on elasticity measurement of low quality factor materials using fem-based resonant ultrasound spectroscopy. J Mech Behav Biomed Mater 124:104848
- Price K, Storn RM, Lampinen JA (2006) Differential evolution: a practical approach to global optimization. Springer
- Tan Z, Tang Y, Li K et al (2022) Differential evolution with hybrid parameters and mutation strategies based on reinforcement learning. Swarm Evol Comput 75:101194
- Li K, Fu X, Wang F et al (2022) A dynamic population reduction differential evolution algorithm combining linear and nonlinear strategy piecewise functions. Concurr Comput Pract Exp 34(6):e6773
- Das S, Suganthan PN (2010) Problem definitions and evaluation criteria for cec 2011 competition on testing evolutionary algorithms on real world optimization problems. Jadavpur Univ Nanyang Technol Univ Kolkata 1:341–359
- Zhang X, Liu Q, Qu Y (2023) An adaptive differential evolution algorithm with population size reduction strategy for unconstrained optimization problem. Appl Soft Comput 138:110209
- Guo SM, Yang CC, Hsu PH et al (2014) Improving differential evolution with a successful-parent-selecting framework. IEEE Trans Evol Comput 19(5):717–730
Cited by
Qin, C., Gao, Y., & Wang, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-16815-7 · Cited by 9
Metadata
- Authors
- Huang, Y., Qian, X., & Song, W.
- Affiliations
- Jiangnan University (CN)
- Corresponding author
- Huang, Yawei
- Venue
- The Journal of Supercomputing
- Published
- 2024-06-22
- Volume
- 80
- Issue
- 15
- Pages
- 22245-22286
- ISSN
- 0920-8542 (ISSN-L)
- 1573-0484
- OpenAlex
- W4399916852
- Cited by
- 5
- References
- 49
- Citations by year
- 2026: 2 · 2025: 3
- FWCI
- 0.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
- Locations
- The Journal of Supercomputing (publishedVersion)
- Funders
- the National Natural Science Foundation of China (62076110)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Multi-Objective Optimization Algorithms
- Keywords
- differential evolution, selection operator, single-objective optimization, CEC benchmark functions
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: Enhancing differential evolution algorithm with a fitness-distance-based selection strategy. https://app.cochat.ai/papers/10.1007/s11227-024-06298-0
- The paper
- Huang, Y., Qian, X., & Song, W. (2024). Enhancing differential evolution algorithm with a fitness-distance-based selection strategy. The Journal of Supercomputing. https://doi.org/10.1007/s11227-024-06298-0
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.
