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Cited by 389

The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.

Modified Grey Wolf Optimizer for Global Engineering Optimization

Open access
Mittal, Nitin, Singh, Urvinder, Sohi, Balwinder Singh·2016·Applied Computational Intelligence and Soft Computing
DOI 10.1155/2016/7950348
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Abstract

Nature-inspired algorithms are becoming popular among researchers due to their simplicity and flexibility. The nature-inspired metaheuristic algorithms are analysed in terms of their key features like their diversity and adaptation, exploration and exploitation, and attractions and diffusion mechanisms. The success and challenges concerning these algorithms are based on their parameter tuning and parameter control. A comparatively new algorithm motivated by the social hierarchy and hunting behavior of grey wolves is Grey Wolf Optimizer (GWO), which is a very successful algorithm for solving real mechanical and optical engineering problems. In the original GWO, half of the iterations are devoted to exploration and the other half are dedicated to exploitation, overlooking the impact of right balance between these two to guarantee an accurate approximation of global optimum. To overcome this shortcoming, a modified GWO (mGWO) is proposed, which focuses on proper balance between exploration and exploitation that leads to an optimal performance of the algorithm. Simulations based on benchmark problems and WSN clustering problem demonstrate the effectiveness, efficiency, and stability of mGWO compared with the basic GWO and some well-known algorithms.

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Show all 46 references

Research works printed but not linked: 9

  1. 10.1049/iet-map.2010.0204 · doi:10.1049/iet-map.2010.0204
  2. 2002, Systems Engineering—Theory & Practice, 22, 32
  3. 10.1504/ijcistudies.2009.025340 · doi:10.1504/ijcistudies.2009.025340
  4. 2007, International Journal of Innovative Computing Information and Control, 3, 163
  5. 2015, Journal of Optimization in Civil Engineering, 5, 511
  6. 2015, Australian Journal of Basic & Applied Science, 9, 150
  7. 10.1007/s13369-015-1641-x · doi:10.1007/s13369-015-1641-x
  8. 10.1016/j.swevo.2011.06.004 · doi:10.1016/j.swevo.2011.06.004
  9. 10.1016/j.asoc.2011.04.007 · doi:10.1016/j.asoc.2011.04.007

Cited by

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Metadata

Authors
Mittal, N., Singh, U., & Sohi, B. S.
Venue
Applied Computational Intelligence and Soft Computing
Publisher
Wiley
Published
2016
Volume
2016
Pages
1-16
ISSN
  • 1687-9724 (ISSN-L)
  • 1687-9732
DOI
10.1155/2016/7950348
OpenAlex
W2345827752
Cited by
389
References
46
FWCI
24.90
Type
journal article
Language
en
Open access
gold
Licence
creativecommons.org/licenses/by/4.0/ (unspecified)

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CoChat. (2026). Citation check: Modified Grey Wolf Optimizer for Global Engineering Optimization. https://app.cochat.ai/papers/10.1155/2016/7950348
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Mittal, N., Singh, U., & Sohi, B. S. (2016). Modified Grey Wolf Optimizer for Global Engineering Optimization. Applied Computational Intelligence and Soft Computing. https://doi.org/10.1155/2016/7950348

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