  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.
