Harris Hawks Algorithm Incorporating Tuna Swarm Algorithm and Differential Variance Strategy Article Swipe
YOU?
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· 2023
· Open Access
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· DOI: https://doi.org/10.1051/wujns/2023286461
Because of the low convergence accuracy of the basic Harris Hawks algorithm, which quickly falls into the local optimal, a Harris Hawks algorithm combining tuna swarm algorithm and differential mutation strategy (TDHHO) is proposed. The escape energy factor of nonlinear periodic energy decline balances the ability of global exploration and regional development. The parabolic foraging approach of the tuna swarm algorithm is introduced to enhance the global exploration ability of the algorithm and accelerate the convergence speed. The difference variation strategy is used to mutate the individual position and calculate the fitness, and the fitness of the original individual position is compared. The greedy technique is used to select the one with better fitness of the objective function, which increases the diversity of the population and improves the possibility of the algorithm jumping out of the local extreme value. The test function tests the TDHHO algorithm, and compared with other optimization algorithms, the experimental results show that the convergence speed and optimization accuracy of the improved Harris Hawks are improved. Finally, the enhanced Harris Hawks algorithm is applied to engineering optimization and wireless sensor networks (WSN) coverage optimization problems, and the feasibility of the TDHHO algorithm in practical application is further verified.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1051/wujns/2023286461
- https://wujns.edpsciences.org/articles/wujns/pdf/2023/06/wujns-1007-1202-2023-06-0461-13.pdf
- OA Status
- hybrid
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390882358
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4390882358Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1051/wujns/2023286461Digital Object Identifier
- Title
-
Harris Hawks Algorithm Incorporating Tuna Swarm Algorithm and Differential Variance StrategyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-01Full publication date if available
- Authors
-
Xiaohan XU, Haima Yang, Heqing ZHENG, Jun Li, Jin Liu, Dawei Zhang, Hongxin HUANGList of authors in order
- Landing page
-
https://doi.org/10.1051/wujns/2023286461Publisher landing page
- PDF URL
-
https://wujns.edpsciences.org/articles/wujns/pdf/2023/06/wujns-1007-1202-2023-06-0461-13.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://wujns.edpsciences.org/articles/wujns/pdf/2023/06/wujns-1007-1202-2023-06-0461-13.pdfDirect OA link when available
- Concepts
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Algorithm, Convergence (economics), Computer science, Differential evolution, Premature convergence, Swarm behaviour, Mathematical optimization, Bat algorithm, Particle swarm optimization, Mathematics, Economics, Economic growthTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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19Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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