Duck swarm algorithm: theory, numerical optimization, and applications Article Swipe
Mengjian Zhang
,
Guihua Wen
,
Jing Yang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2112.13508
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2112.13508
A swarm intelligence-based optimization algorithm, named Duck Swarm Algorithm (DSA), is proposed in this study, which is inspired by the searching for food sources and foraging behaviors of the duck swarm. Two rules are modeled from the finding food and foraging of the duck, which corresponds to the exploration and exploitation phases of the proposed DSA, respectively. The performance of the DSA is verified by using multiple CEC benchmark functions, where its statistical (best, mean, standard deviation, and average running-time) results are compared with seven well-known algorithms like Particle swarm optimization (PSO), Firefly algorithm (FA), Chicken swarm optimization (CSO), Grey wolf optimizer (GWO), Sine cosine algorithm (SCA), and Marine-predators algorithm (MPA), and Archimedes optimization algorithm (AOA). Moreover, the Wilcoxon rank-sum test, Friedman test, and convergence curves of the comparison results are utilized to prove the superiority of the DSA against other algorithms. The results demonstrate that DSA is a high-performance optimization method in terms of convergence speed and exploration-exploitation balance for solving the numerical optimization problems. Also, DSA is applied for the optimal design of six engineering constrained optimization problems and the node optimization deployment task of the Wireless Sensor Network (WSN). Overall, the comparison results revealed that the DSA is a promising and very competitive algorithm for solving different optimization problems.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2112.13508
- https://arxiv.org/pdf/2112.13508
- OA Status
- green
- Cited By
- 6
- References
- 68
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4226307066
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https://openalex.org/W4226307066Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2112.13508Digital Object Identifier
- Title
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Duck swarm algorithm: theory, numerical optimization, and applicationsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-12-27Full publication date if available
- Authors
-
Mengjian Zhang, Guihua Wen, Jing YangList of authors in order
- Landing page
-
https://arxiv.org/abs/2112.13508Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2112.13508Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2112.13508Direct OA link when available
- Concepts
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Firefly algorithm, Algorithm, Particle swarm optimization, Swarm behaviour, Swarm intelligence, Benchmark (surveying), Multi-swarm optimization, Computer science, Convergence (economics), Mathematical optimization, Optimization problem, Mathematics, Economics, Geography, Economic growth, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2023: 5Per-year citation counts (last 5 years)
- References (count)
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68Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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