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Cited by 574

The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.

Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems

Open access
Dehghani, Mohammad, Hubalovsky, Stepan, Trojovsky, Pavel·2021·IEEE Access
DOI 10.1109/access.2021.3133286
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Abstract

Optimization algorithms are one of the effective stochastic methods in solving optimization problems. In this paper, a new swarm-based algorithm called Northern Goshawk Optimization (NGO) algorithm is presented that simulates the behavior of northern goshawk during prey hunting. This hunting strategy includes two phases of prey identification and the tail and chase process. The various steps of the proposed NGO algorithm are described and then its mathematical modeling is presented for use in solving optimization problems. The ability of NGO to solve optimization problems is evaluated on sixty-eight different objective functions. To analyze the quality of the results, the proposed NGO algorithm is compared with eight well-known algorithms, particle swarm optimization, genetic algorithm, teaching-learning based optimization, gravitational search algorithm, grey wolf optimizer, whale optimization algorithm, tunicate swarm algorithm, and marine predators algorithm. In addition, for further analysis, the proposed algorithm is also employed to solve four engineering design problems. The results of simulations and experiments show that the proposed NGO algorithm, by creating a proper balance between exploration and exploitation, has an effective performance in solving optimization problems and is much more competitive than similar algorithms.

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Show all 31 references

Research works printed but not linked: 10

  1. ferguson-lees, 2001, Raptors of the World
  2. 10.1109/ACCESS.2021.3110849 · doi:10.1109/access.2021.3110849
  3. mueller, 1979, The identification of North American accipiters, Amer Birds, 33, 236
  4. del hoyo, 1992, Handbook of the Birds of the World
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  6. 10.1109/ACCESS.2020.2980135 · doi:10.1109/access.2020.2980135
  7. 10.1109/59.317674 · doi:10.1109/59.317674
  8. 10.1016/j.asoc.2020.106268 · doi:10.1016/j.asoc.2020.106268
  9. 10.1109/TEVC.2003.814902 · doi:10.1109/tevc.2003.814902
  10. 10.1016/j.cor.2014.10.011 · doi:10.1016/j.cor.2014.10.011

Cited by

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Metadata

Authors
Dehghani, M., Hubalovsky, S., & Trojovsky, P.
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2021
Volume
9
Pages
162059-162080
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2021.3133286
OpenAlex
W4206385874
Cited by
574
References
31
FWCI
28.93
Type
journal article
Language
en
Open access
gold
Licence
Funders
Excellence Project Faculty of Science (PřF), University of Hradec Králové (2208/2021–2022)

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CoChat. (2026). Citation check: Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems. https://app.cochat.ai/papers/10.1109/access.2021.3133286
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Dehghani, M., Hubalovsky, S., & Trojovsky, P. (2021). Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems. IEEE Access. https://doi.org/10.1109/access.2021.3133286

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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.