Chicken swarm optimization and associated variants: a literature review

Chicken swarm optimization (CSO) is a new metaheuristic algorithm inspired by biologically inspired metaheuristic algorithms that imitate the behavior of chicken flocks. CSO has the advantages of strong global search ability, high stability, and strong multi-subgroup collaborative search ability, and has important research potential. It is widely used in various optimization problems in real life. However, CSO also has some problems, including slow convergence, insufficient local search ability, and easy to fall into local optimality. Therefore, various variants of CSO have been proposed. This article presents a structural review of Chicken Swarm Optimization (CSO) and its variants published between 2014 and 2025. The review systematically organizes and synthesizes existing research by outlining the fundamental principles of CSO, categorizing improvement strategies, and analyzing the characteristics and performance of different variants. Instead of conducting an empirical comprehensive study, this structural review focuses on mapping algorithmic developments, identifying methodological trends, and summarizing application domains such as engineering design, energy systems, image processing, and intelligent diagnosis. Furthermore, the review highlights current limitations of CSO, discusses theoretical considerations, and provides future research directions. The presented structural framework offers clearer insights into the evolution of CSO and serves as a reference for researchers seeking to understand or extend CSO-based methods.