The DOI matches its Crossref record.
A global search strategy of quantum-behaved particle swarm optimization
Abstract
Based on the quantum-behaved particle swarm optimization (QPSO) algorithm, we formulate the philosophy of QPSO and introduce a so-called mainstream thought of the population to evaluate the search scope of a particle and thus propose a novel parameter control method of QPSO. After that, we test the revised QPSO algorithm on several benchmark functions and the experiment results show its superiority.
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Cited by
Sengupta, S., Basak, S., & Peters, R. (2018) · Machine Learning and Knowledge Extraction · doi:10.3390/make1010010 · Cited by 543
Sun, J., Fang, W., Wu, X., Palade, V., & Xu, W. (2012) · Evolutionary Computation · doi:10.1162/evco_a_00049 · Cited by 396
Self-citationEsmin, A. A. A., Coelho, R. A., & Matwin, S. (2015) · Artificial Intelligence Review · doi:10.1007/s10462-013-9400-4 · Cited by 340
Fu, Y., Ding, M., & Zhou, C. (2012) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/tsmca.2011.2159586 · Cited by 256
Li, D., Huang, L., Wang, K., Pang, W., Zhou, Y., & Zhang, R. (2018) · IEEE Access · doi:10.1109/access.2018.2882455 · Cited by 11
Metadata
- Authors
- Sun, J., Xu, W., & Feng, B.
- Venue
- IEEE Conference on Cybernetics and Intelligent Systems, 2004.
- Publisher
- IEEE
- Published
- 2005-07-06
- Volume
- 1
- Pages
- 111-116
- OpenAlex
- W2132775137
- Cited by
- 711
- References
- 0
- FWCI
- 14.79
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A global search strategy of quantum-behaved particle swarm optimization. https://app.cochat.ai/papers/10.1109/iccis.2004.1460396
- The paper
- Sun, J., Xu, W., & Feng, B. (2005). A global search strategy of quantum-behaved particle swarm optimization. IEEE Conference on Cybernetics and Intelligent Systems, 2004. https://doi.org/10.1109/iccis.2004.1460396
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