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.

翻译：本研究提出了一种基于群体智能的优化算法，称为鸭群算法（DSA），其灵感来源于鸭群寻找食物源和觅食的行为。从鸭子的觅食行为中建模出两条规则，分别对应所提DSA的探索与开发阶段。通过使用多个CEC基准函数验证了DSA的性能，并将其统计结果（最佳值、平均值、标准差和平均运行时间）与七种知名算法进行比较，包括粒子群优化（PSO）、萤火虫算法（FA）、鸡群优化（CSO）、灰狼优化器（GWO）、正弦余弦算法（SCA）、海洋捕食者算法（MPA）和阿基米德优化算法（AOA）。此外，利用Wilcoxon秩和检验、Friedman检验以及比较结果的收敛曲线证明了DSA相对于其他算法的优越性。结果表明，在求解数值优化问题时，DSA在收敛速度和探索-开发平衡方面是一种高性能的优化方法。同时，DSA被应用于六个工程约束优化问题的最优设计以及无线传感器网络（WSN）的节点优化部署任务。总体而言，比较结果表明DSA是解决不同优化问题的一种前景广阔且极具竞争力的算法。