Duck swarm algorithm: theory, numerical optimization, and applications

TL;DR

The Duck Swarm Algorithm (DSA), inspired by duck foraging behavior, is a new optimization method that outperforms several existing algorithms in benchmark tests and practical engineering problems.

Contribution

This study introduces the DSA, a novel swarm intelligence algorithm inspired by duck foraging, demonstrating superior performance over established algorithms in various optimization tasks.

Findings

DSA outperforms other algorithms in benchmark tests.

DSA achieves faster convergence and better exploration-exploitation balance.

DSA is effective in engineering design and wireless sensor network optimization.

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.