The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems
Abstract
Surrogate models have shown to be effective in assisting metaheuristic algorithms for solving computationally expensive complex optimization problems. The effectiveness of existing surrogate-assisted metaheuristic algorithms, however, has only been verified on low-dimensional optimization problems. In this paper, a surrogate-assisted cooperative swarm optimization algorithm is proposed, in which a surrogate-assisted particle swarm optimization (PSO) algorithm and a surrogate-assisted social learning-based PSO (SL-PSO) algorithm cooperatively search for the global optimum. The cooperation between the PSO and the SL-PSO consists of two aspects. First, they share promising solutions evaluated by the real fitness function. Second, the SL-PSO focuses on exploration while the PSO concentrates on local search. Empirical studies on six 50-D and six 100-D benchmark problems demonstrate that the proposed algorithm is able to find high-quality solutions for high-dimensional problems on a limited computational budget.
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
A modified particle swarm optimizer
Shi, Y., & Eberhart, R. (2002) · 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360) · doi:10.1109/icec.1998.699146 · Cited by 10,229
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
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
Multiquadric equations of topography and other irregular surfaces
Hardy, R. L. (1971) · Journal of Geophysical Research Atmospheres · doi:10.1029/jb076i008p01905 · Cited by 2,811
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
The swarm and the queen: towards a deterministic and adaptive particle swarm optimization
Clerc, M. (2003) · Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406) · doi:10.1109/cec.1999.785513 · Cited by 1,692
Surrogate-assisted evolutionary computation: Recent advances and future challenges
Jin, Y. (2011) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2011.05.001 · Cited by 1,514
Self-citationKnowles, J. (2006) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2005.851274 · Cited by 1,254
A comprehensive survey of fitness approximation in evolutionary computation
Jin, Y. (2005) · Soft Computing · doi:10.1007/s00500-003-0328-5 · Cited by 1,229
Self-citation
Cited by
Data-Driven Evolutionary Optimization: An Overview and Case Studies
Jin, Y., Wang, H., Chugh, T., Guo, D., & Miettinen, K. (2019) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2018.2869001 · Cited by 686
Self-citationBio-inspired computation: Where we stand and what's next
Del Ser, J., Osaba, E., Molina, D., Yang, X.-S., Salcedo-Sanz, S., Camacho, D., Das, S., Suganthan, P. N., Coello Coello, C. A., & Herrera, F. (2019) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2019.04.008 · Cited by 593
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
RetractedImproved Binary Grey Wolf Optimizer and Its application for feature selection
Hu, P., Pan, J.-S., & Chu, S.-C. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105746 · Cited by 386
Surrogate-assisted hierarchical particle swarm optimization
Yu, H., Tan, Y., Zeng, J., Sun, C., & Jin, Y. (2018) · Information Sciences · doi:10.1016/j.ins.2018.04.062 · Cited by 313
Self-citation
Metadata
- Authors
- Sun, C., Jin, Y., Cheng, R., Ding, J., & Zeng, J.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2017-08
- Volume
- 21
- Issue
- 4
- Pages
- 644-660
- ISSN
- 1089-778X (ISSN-L)
- 1941-0026
- OpenAlex
- W2591927365
- Cited by
- 425
- References
- 42
- FWCI
- 21.16
- Type
- journal article
- 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: Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems. https://app.cochat.ai/papers/10.1109/tevc.2017.2675628
- The paper
- Sun, C., Jin, Y., Cheng, R., Ding, J., & Zeng, J. (2017). Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/tevc.2017.2675628
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.
