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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Veröffentlicht in:Cluster computing Jg. 27; H. 5; S. 6441 - 6469
Hauptverfasser: Zhang, Mengjian, Wen, Guihua
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.08.2024
Springer Nature B.V
Schlagworte:
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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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-024-04293-x