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.

本研究受到鸭群寻找食物源和觅食行为的启发，提出了一种基于群体智能的优化算法，称为鸭群算法（DSA）。两条规则是根据鸭子的寻找食物和觅食建模的，分别对应于所提出的 DSA 的探索和开发阶段。DSA 的性能通过使用多个 CEC 基准函数进行验证，其统计（最佳、平均值、标准差和平均运行时间）结果与粒子群优化 (PSO)、Firefly 算法等七种著名算法进行比较（FA）、鸡群优化（CSO）、灰狼优化（GWO）、正余弦算法（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，DSA 在收敛速度和探索-利用平衡方面是一种解决数值优化问题的高性能优化方法。此外，DSA还应用于六个工程约束优化问题的优化设计和无线传感器网络（WSN）的节点优化部署任务。总体而言，比较结果表明 DSA 是一种很有前途且非常有竞争力的算法，用于解决不同的优化问题。