The DOI matches its Crossref record; 132 of the 132 references it cites were found in a registry or an index.
Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives
Open accessAbstract
Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems that cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social co-operation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment, and improvements of its most basic as well as some of the very recent state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.
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Cites
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UCI Machine Learning Repository
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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
Recent Advances in Tunable Metasurfaces: Materials, Design, and Applications
Abdelraouf, O. A. M., Wang, Z., Liu, H., Dong, Z., Wang, Q., Ye, M., Wang, X. R., Wang, Q. J., & Liu, H. (2022) · ACS Nano · doi:10.1021/acsnano.2c04628 · Cited by 265
Revisiting the global workspace orchestrating the hierarchical organization of the human brain
Deco, G., Vidaurre, D., & Kringelbach, M. L. (2021) · Nature Human Behaviour · doi:10.1038/s41562-020-01003-6 · Cited by 162
Akhter, I., Jalal, A., & Kim, K. (2021) · 2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST) · doi:10.1109/ibcast51254.2021.9393293 · Cited by 58
Metadata
- Authors
- Sengupta, S., Basak, S., & Peters, R.
- Venue
- Machine Learning and Knowledge Extraction
- Publisher
- MDPI AG
- Published
- 2018-10-10
- Volume
- 1
- Issue
- 1
- Pages
- 157-191
- ISSN
- 2504-4990 (ISSN-L)
- OpenAlex
- W2796986051
- Cited by
- 543
- References
- 132
- FWCI
- 31.12
- Type
- journal article
- Language
- en
- Open access
- gold
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