Duck swarm algorithm: theory, numerical optimization, and applications Article Swipe
Mengjian Zhang
,
Guihua Wen
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3537143/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3537143/v1
A swarm intelligence-based optimization algorithm, named Duck Swarm Algorithm (DSA), is proposed in this study. This algorithm is inspired by the searching for food sources and foraging behaviors of the duck swarm. The performance of the DSA is verified by using eighteen 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 used 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 high-dimension optimization functions. Also, DSA is applied for the optimal design of six engineering constraint 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.
Related Topics
Concepts
Firefly algorithm
Particle swarm optimization
Mathematical optimization
Swarm behaviour
Algorithm
Multi-swarm optimization
Meta-optimization
Benchmark (surveying)
Convergence (economics)
Computer science
Swarm intelligence
Optimization problem
Derivative-free optimization
Imperialist competitive algorithm
Test functions for optimization
Mathematics
Geography
Economics
Geodesy
Economic growth
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-3537143/v1
- https://www.researchsquare.com/article/rs-3537143/latest.pdf
- OA Status
- gold
- Cited By
- 7
- References
- 67
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388463663
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388463663Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-3537143/v1Digital Object Identifier
- Title
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Duck swarm algorithm: theory, numerical optimization, and applicationsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-07Full publication date if available
- Authors
-
Mengjian Zhang, Guihua WenList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-3537143/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-3537143/latest.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-3537143/latest.pdfDirect OA link when available
- Concepts
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Firefly algorithm, Particle swarm optimization, Mathematical optimization, Swarm behaviour, Algorithm, Multi-swarm optimization, Meta-optimization, Benchmark (surveying), Convergence (economics), Computer science, Swarm intelligence, Optimization problem, Derivative-free optimization, Imperialist competitive algorithm, Test functions for optimization, Mathematics, Geography, Economics, Geodesy, Economic growthTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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67Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.seven | 59 |
| abstract_inverted_index.speed | 131 |
| abstract_inverted_index.swarm | 2, 64, 71 |
| abstract_inverted_index.terms | 128 |
| abstract_inverted_index.test, | 95, 97 |
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| abstract_inverted_index.where | 45 |
| abstract_inverted_index.(AOA). | 90 |
| abstract_inverted_index.(CSO), | 73 |
| abstract_inverted_index.(DSA), | 10 |
| abstract_inverted_index.(GWO), | 77 |
| abstract_inverted_index.(MPA), | 85 |
| abstract_inverted_index.(PSO), | 66 |
| abstract_inverted_index.(SCA), | 81 |
| abstract_inverted_index.(WSN). | 164 |
| abstract_inverted_index.(best, | 48 |
| abstract_inverted_index.Sensor | 162 |
| abstract_inverted_index.cosine | 79 |
| abstract_inverted_index.curves | 100 |
| abstract_inverted_index.design | 147 |
| abstract_inverted_index.method | 126 |
| abstract_inverted_index.study. | 15 |
| abstract_inverted_index.swarm. | 32 |
| abstract_inverted_index.Chicken | 70 |
| abstract_inverted_index.Firefly | 67 |
| abstract_inverted_index.Network | 163 |
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| abstract_inverted_index.applied | 143 |
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| abstract_inverted_index.solving | 136, 181 |
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| abstract_inverted_index.inspired | 19 |
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| abstract_inverted_index.Moreover, | 91 |
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| abstract_inverted_index.behaviors | 28 |
| abstract_inverted_index.benchmark | 43 |
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| abstract_inverted_index.problems. | 184 |
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| abstract_inverted_index.Marine-predators | 83 |
| abstract_inverted_index.high-performance | 124 |
| abstract_inverted_index.intelligence-based | 3 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| abstract_inverted_index.exploration-exploitation | 133 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/14 |
| sustainable_development_goals[0].score | 0.7400000095367432 |
| sustainable_development_goals[0].display_name | Life below water |
| citation_normalized_percentile.value | 0.85740557 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |