The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Multiobjective optimization using dynamic neighborhood particle swarm optimization
Abstract
This paper presents a particle swarm optimization (PSO) algorithm for multiobjective optimization problems. PSO is modified by using a dynamic neighborhood strategy, new particle memory updating, and one-dimension optimization to deal with multiple objectives. Several benchmark cases were tested and showed that PSO could efficiently find multiple Pareto optimal solutions.
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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
Self-citation
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Dynamic multi-swarm particle swarm optimizer
Liang, J. J., & Suganthan, P. N. (2005) · Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005. · doi:10.1109/sis.2005.1501611 · Cited by 486
Metadata
- Authors
- Hu, X., & Eberhart, R.
- Venue
- Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)
- Publisher
- IEEE
- Published
- 2003-06-25
- Volume
- 2
- Pages
- 1677-1681
- OpenAlex
- W2157601049
- Cited by
- 613
- References
- 1
- FWCI
- 18.13
- Type
- conference paper
- Language
- en
- Open access
- closed
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- The paper
- Hu, X., & Eberhart, R. (2003). Multiobjective optimization using dynamic neighborhood particle swarm optimization. Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600). https://doi.org/10.1109/cec.2002.1004494
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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.
