Duck swarm algorithm: theory, numerical optimization, and applications

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...

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Published in:	Cluster computing Vol. 27; no. 5; pp. 6441 - 6469
Main Authors:	Zhang, Mengjian, Wen, Guihua
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.08.2024 Springer Nature B.V
Subjects:	Algorithms Animals Computer Communication Networks Computer Science Convergence Design optimization Exploitation Food Foraging behavior Genetic algorithms Heuristic Heuristic methods Mutation Operating Systems Optimization algorithms Optimization techniques Particle swarm optimization Physics Processor Architectures Swarm intelligence Wireless sensor networks Wolves Wireless sensor network Diversity analysis Swarm intelligence Duck swarm algorithm Constrained optimization problems
ISSN:	1386-7857, 1573-7543
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Abstract	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.
AbstractList	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.
Author	Wen, Guihua Zhang, Mengjian
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Snippet	A swarm intelligence-based optimization algorithm, named Duck Swarm Algorithm (DSA), is proposed in this study, which is inspired by the searching for food...
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SubjectTerms	Algorithms Animals Computer Communication Networks Computer Science Convergence Design optimization Exploitation Food Foraging behavior Genetic algorithms Heuristic Heuristic methods Mutation Operating Systems Optimization algorithms Optimization techniques Particle swarm optimization Physics Processor Architectures Swarm intelligence Wireless sensor networks Wolves
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Title	Duck swarm algorithm: theory, numerical optimization, and applications
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